--- _id: '13080' abstract: - lang: eng text: "Data for the manuscript 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire' ([2006.01275] Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire (arxiv.org))\r\n\r\nWe upload a pdf with extended data sets, and the raw data for these extended datasets as well." article_processing_charge: No author: - first_name: Denise full_name: Puglia, Denise id: 4D495994-AE37-11E9-AC72-31CAE5697425 last_name: Puglia - first_name: Esteban full_name: Martinez, Esteban last_name: Martinez - first_name: Gerbold full_name: Menard, Gerbold last_name: Menard - first_name: Andreas full_name: Pöschl, Andreas last_name: Pöschl - first_name: Sergei full_name: Gronin, Sergei last_name: Gronin - first_name: Geoffrey full_name: Gardner, Geoffrey last_name: Gardner - first_name: Ray full_name: Kallaher, Ray last_name: Kallaher - first_name: Michael full_name: Manfra, Michael last_name: Manfra - first_name: Charles full_name: Marcus, Charles last_name: Marcus - first_name: Andrew P full_name: Higginbotham, Andrew P id: 4AD6785A-F248-11E8-B48F-1D18A9856A87 last_name: Higginbotham orcid: 0000-0003-2607-2363 - first_name: Lucas full_name: Casparis, Lucas last_name: Casparis citation: ama: Puglia D, Martinez E, Menard G, et al. Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire. 2021. doi:10.5281/ZENODO.4592435 apa: Puglia, D., Martinez, E., Menard, G., Pöschl, A., Gronin, S., Gardner, G., … Casparis, L. (2021). Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire. Zenodo. https://doi.org/10.5281/ZENODO.4592435 chicago: Puglia, Denise, Esteban Martinez, Gerbold Menard, Andreas Pöschl, Sergei Gronin, Geoffrey Gardner, Ray Kallaher, et al. “Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.4592435. ieee: D. Puglia et al., “Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire.” Zenodo, 2021. ista: Puglia D, Martinez E, Menard G, Pöschl A, Gronin S, Gardner G, Kallaher R, Manfra M, Marcus C, Higginbotham AP, Casparis L. 2021. Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire, Zenodo, 10.5281/ZENODO.4592435. mla: Puglia, Denise, et al. Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire. Zenodo, 2021, doi:10.5281/ZENODO.4592435. short: D. Puglia, E. Martinez, G. Menard, A. Pöschl, S. Gronin, G. Gardner, R. Kallaher, M. Manfra, C. Marcus, A.P. Higginbotham, L. Casparis, (2021). date_created: 2023-05-23T17:11:28Z date_published: 2021-03-09T00:00:00Z date_updated: 2023-08-08T14:08:07Z day: '09' ddc: - '530' department: - _id: AnHi doi: 10.5281/ZENODO.4592435 main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.4592460 month: '03' oa: 1 oa_version: Published Version publisher: Zenodo related_material: link: - relation: software url: https://github.com/caslu85/Induced-Gap-Closing-Shared/tree/1.1.3 record: - id: '9570' relation: used_in_publication status: public status: public title: Data for 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '13146' abstract: - lang: eng text: 'A recent line of work has analyzed the theoretical properties of deep neural networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue of the NTK has been related to the memorization capacity, the global convergence of gradient descent algorithms and the generalization of deep nets. However, existing results either provide bounds in the two-layer setting or assume that the spectrum of the NTK matrices is bounded away from 0 for multi-layer networks. In this paper, we provide tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU nets, both in the limiting case of infinite widths and for finite widths. In the finite-width setting, the network architectures we consider are fairly general: we require the existence of a wide layer with roughly order of N neurons, N being the number of data samples; and the scaling of the remaining layer widths is arbitrary (up to logarithmic factors). To obtain our results, we analyze various quantities of independent interest: we give lower bounds on the smallest singular value of hidden feature matrices, and upper bounds on the Lipschitz constant of input-output feature maps.' acknowledgement: The authors would like to thank the anonymous reviewers for their helpful comments. MM was partially supported by the 2019 Lopez-Loreta Prize. QN and GM acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 757983). article_processing_charge: No arxiv: 1 author: - first_name: Quynh full_name: Nguyen, Quynh last_name: Nguyen - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 - first_name: Guido full_name: Montufar, Guido last_name: Montufar citation: ama: 'Nguyen Q, Mondelli M, Montufar G. Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:8119-8129.' apa: 'Nguyen, Q., Mondelli, M., & Montufar, G. (2021). Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.' chicago: Nguyen, Quynh, Marco Mondelli, and Guido Montufar. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” In Proceedings of the 38th International Conference on Machine Learning, 139:8119–29. ML Research Press, 2021. ieee: Q. Nguyen, M. Mondelli, and G. Montufar, “Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 8119–8129. ista: Nguyen Q, Mondelli M, Montufar G. 2021. Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 8119–8129. mla: Nguyen, Quynh, et al. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 8119–29. short: Q. Nguyen, M. Mondelli, G. Montufar, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 8119–8129. conference: end_date: 2021-07-24 location: Virtual name: International Conference on Machine Learning start_date: 2021-07-18 date_created: 2023-06-18T22:00:48Z date_published: 2021-07-01T00:00:00Z date_updated: 2024-09-10T13:03:17Z day: '01' ddc: - '000' department: - _id: MaMo external_id: arxiv: - '2012.11654' file: - access_level: open_access checksum: 19489cf5e16a0596b1f92e317d97c9b0 content_type: application/pdf creator: dernst date_created: 2023-06-19T10:49:12Z date_updated: 2023-06-19T10:49:12Z file_id: '13155' file_name: 2021_PMLR_Nguyen.pdf file_size: 591332 relation: main_file success: 1 file_date_updated: 2023-06-19T10:49:12Z has_accepted_license: '1' intvolume: ' 139' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '07' oa: 1 oa_version: Published Version page: 8119-8129 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: Proceedings of the 38th International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 isbn: - '9781713845065' publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 139 year: '2021' ... --- _id: '13147' abstract: - lang: eng text: "We investigate fast and communication-efficient algorithms for the classic problem of minimizing a sum of strongly convex and smooth functions that are distributed among n\r\n different nodes, which can communicate using a limited number of bits. Most previous communication-efficient approaches for this problem are limited to first-order optimization, and therefore have \\emph{linear} dependence on the condition number in their communication complexity. We show that this dependence is not inherent: communication-efficient methods can in fact have sublinear dependence on the condition number. For this, we design and analyze the first communication-efficient distributed variants of preconditioned gradient descent for Generalized Linear Models, and for Newton’s method. Our results rely on a new technique for quantizing both the preconditioner and the descent direction at each step of the algorithms, while controlling their convergence rate. We also validate our findings experimentally, showing faster convergence and reduced communication relative to previous methods." acknowledgement: The authors would like to thank Janne Korhonen, Aurelien Lucchi, Celestine MendlerDunner and Antonio Orvieto for helpful discussions. FA ¨and DA were supported during this work by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). PD was supported by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No. 754411. article_processing_charge: No arxiv: 1 author: - first_name: Foivos full_name: Alimisis, Foivos last_name: Alimisis - first_name: Peter full_name: Davies, Peter id: 11396234-BB50-11E9-B24C-90FCE5697425 last_name: Davies orcid: 0000-0002-5646-9524 - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization with quantized preconditioners. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:196-206.' apa: 'Alimisis, F., Davies, P., & Alistarh, D.-A. (2021). Communication-efficient distributed optimization with quantized preconditioners. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 196–206). Virtual: ML Research Press.' chicago: Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” In Proceedings of the 38th International Conference on Machine Learning, 139:196–206. ML Research Press, 2021. ieee: F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206. ista: Alimisis F, Davies P, Alistarh D-A. 2021. Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 196–206. mla: Alimisis, Foivos, et al. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 196–206. short: F. Alimisis, P. Davies, D.-A. Alistarh, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 196–206. conference: end_date: 2021-07-24 location: Virtual name: International Conference on Machine Learning start_date: 2021-07-18 date_created: 2023-06-18T22:00:48Z date_published: 2021-07-01T00:00:00Z date_updated: 2023-06-19T10:44:38Z day: '01' ddc: - '000' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2102.07214' file: - access_level: open_access checksum: 7ec0d59bac268b49c76bf2e036dedd7a content_type: application/pdf creator: dernst date_created: 2023-06-19T10:41:05Z date_updated: 2023-06-19T10:41:05Z file_id: '13154' file_name: 2021_PMLR_Alimisis.pdf file_size: 429087 relation: main_file success: 1 file_date_updated: 2023-06-19T10:41:05Z has_accepted_license: '1' intvolume: ' 139' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 196-206 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Proceedings of the 38th International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 isbn: - '9781713845065' publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Communication-efficient distributed optimization with quantized preconditioners tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 139 year: '2021' ... --- _id: '14117' abstract: - lang: eng text: 'The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.' article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Bernhard full_name: Scholkopf, Bernhard last_name: Scholkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer - first_name: Nan Rosemary full_name: Ke, Nan Rosemary last_name: Ke - first_name: Nal full_name: Kalchbrenner, Nal last_name: Kalchbrenner - first_name: Anirudh full_name: Goyal, Anirudh last_name: Goyal - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio citation: ama: Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. Proceedings of the IEEE. 2021;109(5):612-634. doi:10.1109/jproc.2021.3058954 apa: Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/jproc.2021.3058954 chicago: Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.” Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/jproc.2021.3058954. ieee: B. Scholkopf et al., “Toward causal representation learning,” Proceedings of the IEEE, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021. ista: Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634. mla: Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” Proceedings of the IEEE, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:10.1109/jproc.2021.3058954. short: B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634. date_created: 2023-08-21T12:19:30Z date_published: 2021-05-01T00:00:00Z date_updated: 2023-09-11T11:43:35Z day: '01' department: - _id: FrLo doi: 10.1109/jproc.2021.3058954 extern: '1' external_id: arxiv: - '2102.11107' intvolume: ' 109' issue: '5' keyword: - Electrical and Electronic Engineering language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1109/JPROC.2021.3058954 month: '05' oa: 1 oa_version: Published Version page: 612-634 publication: Proceedings of the IEEE publication_identifier: eissn: - 1558-2256 issn: - 0018-9219 publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: Toward causal representation learning type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 109 year: '2021' ... --- _id: '14176' abstract: - lang: eng text: "Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series data augmentation techniques with a novel contrastive\r\nlearning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Hugo full_name: Yèche, Hugo last_name: Yèche - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Matthias full_name: Hüser, Matthias last_name: Hüser - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch citation: ama: 'Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive learning applied to online patient monitoring. In: Proceedings of 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:11964-11974.' apa: 'Yèche, H., Dresdner, G., Locatello, F., Hüser, M., & Rätsch, G. (2021). Neighborhood contrastive learning applied to online patient monitoring. In Proceedings of 38th International Conference on Machine Learning (Vol. 139, pp. 11964–11974). Virtual: ML Research Press.' chicago: Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” In Proceedings of 38th International Conference on Machine Learning, 139:11964–74. ML Research Press, 2021. ieee: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in Proceedings of 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 11964–11974. ista: Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive learning applied to online patient monitoring. Proceedings of 38th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 139, 11964–11974. mla: Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” Proceedings of 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 11964–74. short: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974. conference: end_date: 2021-07-24 location: Virtual name: International Conference on Machine Learning start_date: 2021-07-18 date_created: 2023-08-22T14:03:04Z date_published: 2021-08-01T00:00:00Z date_updated: 2023-09-11T10:16:55Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2106.05142' intvolume: ' 139' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2106.05142 month: '08' oa: 1 oa_version: Preprint page: 11964-11974 publication: Proceedings of 38th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Neighborhood contrastive learning applied to online patient monitoring type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 139 year: '2021' ... --- _id: '14177' abstract: - lang: eng text: "The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained model with a small number of labels." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Elliot full_name: Creager, Elliot last_name: Creager - first_name: Niki full_name: Kilbertus, Niki last_name: Kilbertus - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Anirudh full_name: Goyal, Anirudh last_name: Goyal - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer citation: ama: 'Träuble F, Creager E, Kilbertus N, et al. On disentangled representations learned from correlated data. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:10401-10412.' apa: 'Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., … Bauer, S. (2021). On disentangled representations learned from correlated data. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 10401–10412). Virtual: ML Research Press.' chicago: Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled Representations Learned from Correlated Data.” In Proceedings of the 38th International Conference on Machine Learning, 139:10401–12. ML Research Press, 2021. ieee: F. Träuble et al., “On disentangled representations learned from correlated data,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 10401–10412. ista: 'Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf B, Bauer S. 2021. On disentangled representations learned from correlated data. Proceedings of the 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 139, 10401–10412.' mla: Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated Data.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 10401–12. short: F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412. conference: end_date: 2021-07-24 location: Virtual name: 'ICML: International Conference on Machine Learning' start_date: 2021-07-18 date_created: 2023-08-22T14:03:47Z date_published: 2021-08-01T00:00:00Z date_updated: 2023-09-11T10:18:48Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2006.07886' intvolume: ' 139' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2006.07886 month: '08' oa: 1 oa_version: Published Version page: 10401-10412 publication: Proceedings of the 38th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: On disentangled representations learned from correlated data type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 139 year: '2021' ... --- _id: '14178' abstract: - lang: eng text: Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance. article_processing_charge: No arxiv: 1 author: - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Manuel full_name: Wüthrich, Manuel last_name: Wüthrich - first_name: Vaibhav full_name: Agrawal, Vaibhav last_name: Agrawal - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled representations in realistic settings. In: The Ninth International Conference on Learning Representations. ; 2021.' apa: Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., … Schölkopf, B. (2021). On the transfer of disentangled representations in realistic settings. In The Ninth International Conference on Learning Representations. Virtual. chicago: Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer of Disentangled Representations in Realistic Settings.” In The Ninth International Conference on Learning Representations, 2021. ieee: A. Dittadi et al., “On the transfer of disentangled representations in realistic settings,” in The Ninth International Conference on Learning Representations, Virtual, 2021. ista: 'Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic settings. The Ninth International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in Realistic Settings.” The Ninth International Conference on Learning Representations, 2021. short: A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021. conference: end_date: 2021-05-07 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2021-05-03 date_created: 2023-08-22T14:04:16Z date_published: 2021-05-04T00:00:00Z date_updated: 2023-09-11T10:55:30Z day: '04' department: - _id: FrLo extern: '1' external_id: arxiv: - '2010.14407' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2010.14407 month: '05' oa: 1 oa_version: Preprint publication: The Ninth International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: On the transfer of disentangled representations in realistic settings type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14179' abstract: - lang: eng text: Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice. article_processing_charge: No arxiv: 1 author: - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Yash full_name: Sharma, Yash last_name: Sharma - first_name: Luigi full_name: Gresele, Luigi last_name: Gresele - first_name: Wieland full_name: Brendel, Wieland last_name: Brendel - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Michel full_name: Besserve, Michel last_name: Besserve - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with data augmentations provably isolates content from style. In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:16451-16467.' apa: Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., & Locatello, F. (2021). Self-supervised learning with data augmentations provably isolates content from style. In Advances in Neural Information Processing Systems (Vol. 34, pp. 16451–16467). Virtual. chicago: Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” In Advances in Neural Information Processing Systems, 34:16451–67, 2021. ieee: J. von Kügelgen et al., “Self-supervised learning with data augmentations provably isolates content from style,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 16451–16467. ista: 'Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M, Locatello F. 2021. Self-supervised learning with data augmentations provably isolates content from style. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 16451–16467.' mla: Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 16451–67. short: J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-07 date_created: 2023-08-22T14:04:36Z date_published: 2021-06-08T00:00:00Z date_updated: 2023-09-11T10:33:19Z day: '08' department: - _id: FrLo extern: '1' external_id: arxiv: - '2106.04619' intvolume: ' 34' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2106.04619 month: '06' oa: 1 oa_version: Preprint page: 16451-16467 publication: Advances in Neural Information Processing Systems publication_identifier: isbn: - '9781713845393' publication_status: published quality_controlled: '1' status: public title: Self-supervised learning with data augmentations provably isolates content from style type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 34 year: '2021' ... --- _id: '14180' abstract: - lang: eng text: 'Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization. ' article_processing_charge: No arxiv: 1 author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Muhammad Waleed full_name: Gondal, Muhammad Waleed last_name: Gondal - first_name: Shruti full_name: Joshi, Shruti last_name: Joshi - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters. In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:10985-10998.' apa: Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F., & Schölkopf, B. (2021). Dynamic inference with neural interpreters. In Advances in Neural Information Processing Systems (Vol. 34, pp. 10985–10998). Virtual. chicago: Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural Interpreters.” In Advances in Neural Information Processing Systems, 34:10985–98, 2021. ieee: N. Rahaman et al., “Dynamic inference with neural interpreters,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 10985–10998. ista: 'Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.' mla: Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 10985–98. short: N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-07 date_created: 2023-08-22T14:04:55Z date_published: 2021-10-12T00:00:00Z date_updated: 2023-09-11T11:33:46Z day: '12' department: - _id: FrLo extern: '1' external_id: arxiv: - '2110.06399' intvolume: ' 34' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2110.06399 month: '10' oa: 1 oa_version: Preprint page: 10985-10998 publication: Advances in Neural Information Processing Systems publication_identifier: isbn: - '9781713845393' publication_status: published quality_controlled: '1' status: public title: Dynamic inference with neural interpreters type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 34 year: '2021' ... --- _id: '14181' abstract: - lang: eng text: Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. The main obstacle to widespread adoption of Boosting Variational Inference is the amount of resources necessary to improve over a strong Variational Inference baseline. In our work, we trace this limitation back to the global curvature of the KL-divergence. We characterize how the global curvature impacts time and memory consumption, address the problem with the notion of local curvature, and provide a novel approximate backtracking algorithm for estimating local curvature. We give new theoretical convergence rates for our algorithms and provide experimental validation on synthetic and real-world datasets. article_processing_charge: No arxiv: 1 author: - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Saurav full_name: Shekhar, Saurav last_name: Shekhar - first_name: Fabian full_name: Pedregosa, Fabian last_name: Pedregosa - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch citation: ama: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational inference with locally adaptive step-sizes. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence; 2021:2337-2343. doi:10.24963/ijcai.2021/322' apa: 'Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., & Rätsch, G. (2021). Boosting variational inference with locally adaptive step-sizes. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/322' chicago: Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2337–43. International Joint Conferences on Artificial Intelligence, 2021. https://doi.org/10.24963/ijcai.2021/322. ieee: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 2021, pp. 2337–2343. ista: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conference on Artificial Intelligence, 2337–2343.' mla: Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–43, doi:10.24963/ijcai.2021/322. short: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343. conference: end_date: 2021-08-27 location: Montreal, Canada name: 'IJCAI: International Joint Conference on Artificial Intelligence' start_date: 2021-08-19 date_created: 2023-08-22T14:05:14Z date_published: 2021-05-19T00:00:00Z date_updated: 2023-09-11T11:14:30Z day: '19' department: - _id: FrLo doi: 10.24963/ijcai.2021/322 extern: '1' external_id: arxiv: - '2105.09240' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2105.09240 month: '05' oa: 1 oa_version: Published Version page: 2337-2343 publication: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence publication_identifier: eisbn: - '9780999241196' publication_status: published publisher: International Joint Conferences on Artificial Intelligence quality_controlled: '1' status: public title: Boosting variational inference with locally adaptive step-sizes type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14182' abstract: - lang: eng text: "When machine learning systems meet real world applications, accuracy is only\r\none of several requirements. In this paper, we assay a complementary\r\nperspective originating from the increasing availability of pre-trained and\r\nregularly improving state-of-the-art models. While new improved models develop\r\nat a fast pace, downstream tasks vary more slowly or stay constant. Assume that\r\nwe have a large unlabelled data set for which we want to maintain accurate\r\npredictions. Whenever a new and presumably better ML models becomes available,\r\nwe encounter two problems: (i) given a limited budget, which data points should\r\nbe re-evaluated using the new model?; and (ii) if the new predictions differ\r\nfrom the current ones, should we update? Problem (i) is about compute cost,\r\nwhich matters for very large data sets and models. Problem (ii) is about\r\nmaintaining consistency of the predictions, which can be highly relevant for\r\ndownstream applications; our demand is to avoid negative flips, i.e., changing\r\ncorrect to incorrect predictions. In this paper, we formalize the Prediction\r\nUpdate Problem and present an efficient probabilistic approach as answer to the\r\nabove questions. In extensive experiments on standard classification benchmark\r\ndata sets, we show that our method outperforms alternative strategies along key\r\nmetrics for backward-compatible prediction updates." article_processing_charge: No arxiv: 1 author: - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Peter full_name: Gehler, Peter last_name: Gehler citation: ama: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. Backward-compatible prediction updates: A probabilistic approach. In: 35th Conference on Neural Information Processing Systems. Vol 34. ; 2021:116-128.' apa: 'Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf, B., & Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic approach. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 116–128). Virtual.' chicago: 'Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” In 35th Conference on Neural Information Processing Systems, 34:116–28, 2021.' ieee: 'F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 116–128.' ista: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 116–128.' mla: 'Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” 35th Conference on Neural Information Processing Systems, vol. 34, 2021, pp. 116–28.' short: F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-07 date_created: 2023-08-22T14:05:41Z date_published: 2021-07-02T00:00:00Z date_updated: 2023-09-11T11:31:59Z day: '02' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.01057' intvolume: ' 34' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2107.01057 month: '07' oa: 1 oa_version: Preprint page: 116-128 publication: 35th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713845393' publication_status: published quality_controlled: '1' status: public title: 'Backward-compatible prediction updates: A probabilistic approach' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 34 year: '2021' ... --- _id: '14221' abstract: - lang: eng text: 'The world is structured in countless ways. It may be prudent to enforce corresponding structural properties to a learning algorithm''s solution, such as incorporating prior beliefs, natural constraints, or causal structures. Doing so may translate to faster, more accurate, and more flexible models, which may directly relate to real-world impact. In this dissertation, we consider two different research areas that concern structuring a learning algorithm''s solution: when the structure is known and when it has to be discovered.' article_number: '2111.13693' article_processing_charge: No arxiv: 1 author: - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: Locatello F. Enforcing and discovering structure in machine learning. arXiv. doi:10.48550/arXiv.2111.13693 apa: Locatello, F. (n.d.). Enforcing and discovering structure in machine learning. arXiv. https://doi.org/10.48550/arXiv.2111.13693 chicago: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2111.13693. ieee: F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv. . ista: Locatello F. Enforcing and discovering structure in machine learning. arXiv, 2111.13693. mla: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” ArXiv, 2111.13693, doi:10.48550/arXiv.2111.13693. short: F. Locatello, ArXiv (n.d.). date_created: 2023-08-22T14:23:35Z date_published: 2021-11-26T00:00:00Z date_updated: 2023-09-12T07:04:44Z day: '26' department: - _id: FrLo doi: 10.48550/arXiv.2111.13693 extern: '1' external_id: arxiv: - '2111.13693' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2111.13693 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Enforcing and discovering structure in machine learning type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14278' abstract: - lang: eng text: 'The Birkhoff conjecture says that the boundary of a strictly convex integrable billiard table is necessarily an ellipse. In this article, we consider a stronger notion of integrability, namely, integrability close to the boundary, and prove a local version of this conjecture: a small perturbation of almost every ellipse that preserves integrability near the boundary, is itself an ellipse. We apply this result to study local spectral rigidity of ellipses using the connection between the wave trace of the Laplacian and the dynamics near the boundary and establish rigidity for almost all of them.' article_number: '2111.12171' article_processing_charge: No arxiv: 1 author: - first_name: Illya full_name: Koval, Illya id: 2eed1f3b-896a-11ed-bdf8-93c7c4bf159e last_name: Koval citation: ama: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse. arXiv. doi:10.48550/ARXIV.2111.12171 apa: Koval, I. (n.d.). Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse. arXiv. https://doi.org/10.48550/ARXIV.2111.12171 chicago: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity of Almost Every Ellipse.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2111.12171. ieee: I. Koval, “Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse,” arXiv. . ista: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse. arXiv, 2111.12171. mla: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity of Almost Every Ellipse.” ArXiv, 2111.12171, doi:10.48550/ARXIV.2111.12171. short: I. Koval, ArXiv (n.d.). date_created: 2023-09-06T08:35:43Z date_published: 2021-11-23T00:00:00Z date_updated: 2023-09-15T06:44:00Z day: '23' department: - _id: GradSch doi: 10.48550/ARXIV.2111.12171 external_id: arxiv: - '2111.12171' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2111.12171 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14332' abstract: - lang: eng text: Learning data representations that are useful for various downstream tasks is a cornerstone of artificial intelligence. While existing methods are typically evaluated on downstream tasks such as classification or generative image quality, we propose to assess representations through their usefulness in downstream control tasks, such as reaching or pushing objects. By training over 10,000 reinforcement learning policies, we extensively evaluate to what extent different representation properties affect out-of-distribution (OOD) generalization. Finally, we demonstrate zero-shot transfer of these policies from simulation to the real world, without any domain randomization or fine-tuning. This paper aims to establish the first systematic characterization of the usefulness of learned representations for real-world OOD downstream tasks. article_processing_charge: No author: - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Manuel full_name: Wuthrich, Manuel last_name: Wuthrich - first_name: Felix full_name: Widmaier, Felix last_name: Widmaier - first_name: Peter Vincent full_name: Gehler, Peter Vincent last_name: Gehler - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Olivier full_name: Bachem, Olivier last_name: Bachem - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer citation: ama: 'Träuble F, Dittadi A, Wuthrich M, et al. Representation learning for out-of-distribution generalization in reinforcement learning. In: ICML 2021 Workshop on Unsupervised Reinforcement Learning. ; 2021.' apa: Träuble, F., Dittadi, A., Wuthrich, M., Widmaier, F., Gehler, P. V., Winther, O., … Bauer, S. (2021). Representation learning for out-of-distribution generalization in reinforcement learning. In ICML 2021 Workshop on Unsupervised Reinforcement Learning. Virtual. chicago: Träuble, Frederik, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” In ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021. ieee: F. Träuble et al., “Representation learning for out-of-distribution generalization in reinforcement learning,” in ICML 2021 Workshop on Unsupervised Reinforcement Learning, Virtual, 2021. ista: 'Träuble F, Dittadi A, Wuthrich M, Widmaier F, Gehler PV, Winther O, Locatello F, Bachem O, Schölkopf B, Bauer S. 2021. Representation learning for out-of-distribution generalization in reinforcement learning. ICML 2021 Workshop on Unsupervised Reinforcement Learning. ICML: International Conference on Machine Learning.' mla: Träuble, Frederik, et al. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021. short: F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021. conference: end_date: 2021-07-23 location: Virtual name: 'ICML: International Conference on Machine Learning' start_date: 2021-07-23 date_created: 2023-09-13T12:43:14Z date_published: 2021-07-23T00:00:00Z date_updated: 2023-09-13T12:44:00Z day: '23' department: - _id: FrLo extern: '1' language: - iso: eng month: '07' oa_version: None publication: ICML 2021 Workshop on Unsupervised Reinforcement Learning publication_status: published quality_controlled: '1' status: public title: Representation learning for out-of-distribution generalization in reinforcement learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14800' abstract: - lang: eng text: 'Research on two-dimensional (2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief background introduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials (PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field. ' article_number: '2108017' article_processing_charge: No article_type: review author: - first_name: Cheng full_name: Chang, Cheng id: 9E331C2E-9F27-11E9-AE48-5033E6697425 last_name: Chang orcid: 0000-0002-9515-4277 - first_name: Wei full_name: Chen, Wei last_name: Chen - first_name: Ye full_name: Chen, Ye last_name: Chen - first_name: Yonghua full_name: Chen, Yonghua last_name: Chen - first_name: Yu full_name: Chen, Yu last_name: Chen - first_name: Feng full_name: Ding, Feng last_name: Ding - first_name: Chunhai full_name: Fan, Chunhai last_name: Fan - first_name: Hong Jin full_name: Fan, Hong Jin last_name: Fan - first_name: Zhanxi full_name: Fan, Zhanxi last_name: Fan - first_name: Cheng full_name: Gong, Cheng last_name: Gong - first_name: Yongji full_name: Gong, Yongji last_name: Gong - first_name: Qiyuan full_name: He, Qiyuan last_name: He - first_name: Xun full_name: Hong, Xun last_name: Hong - first_name: Sheng full_name: Hu, Sheng last_name: Hu - first_name: Weida full_name: Hu, Weida last_name: Hu - first_name: Wei full_name: Huang, Wei last_name: Huang - first_name: Yuan full_name: Huang, Yuan last_name: Huang - first_name: Wei full_name: Ji, Wei last_name: Ji - first_name: Dehui full_name: Li, Dehui last_name: Li - first_name: Lain Jong full_name: Li, Lain Jong last_name: Li - first_name: Qiang full_name: Li, Qiang last_name: Li - first_name: Li full_name: Lin, Li last_name: Lin - first_name: Chongyi full_name: Ling, Chongyi last_name: Ling - first_name: Minghua full_name: Liu, Minghua last_name: Liu - first_name: 'Nan' full_name: Liu, Nan last_name: Liu - first_name: Zhuang full_name: Liu, Zhuang last_name: Liu - first_name: Kian Ping full_name: Loh, Kian Ping last_name: Loh - first_name: Jianmin full_name: Ma, Jianmin last_name: Ma - first_name: Feng full_name: Miao, Feng last_name: Miao - first_name: Hailin full_name: Peng, Hailin last_name: Peng - first_name: Mingfei full_name: Shao, Mingfei last_name: Shao - first_name: Li full_name: Song, Li last_name: Song - first_name: Shao full_name: Su, Shao last_name: Su - first_name: Shuo full_name: Sun, Shuo last_name: Sun - first_name: Chaoliang full_name: Tan, Chaoliang last_name: Tan - first_name: Zhiyong full_name: Tang, Zhiyong last_name: Tang - first_name: Dingsheng full_name: Wang, Dingsheng last_name: Wang - first_name: Huan full_name: Wang, Huan last_name: Wang - first_name: Jinlan full_name: Wang, Jinlan last_name: Wang - first_name: Xin full_name: Wang, Xin last_name: Wang - first_name: Xinran full_name: Wang, Xinran last_name: Wang - first_name: Andrew T.S. full_name: Wee, Andrew T.S. last_name: Wee - first_name: Zhongming full_name: Wei, Zhongming last_name: Wei - first_name: Yuen full_name: Wu, Yuen last_name: Wu - first_name: Zhong Shuai full_name: Wu, Zhong Shuai last_name: Wu - first_name: Jie full_name: Xiong, Jie last_name: Xiong - first_name: Qihua full_name: Xiong, Qihua last_name: Xiong - first_name: Weigao full_name: Xu, Weigao last_name: Xu - first_name: Peng full_name: Yin, Peng last_name: Yin - first_name: Haibo full_name: Zeng, Haibo last_name: Zeng - first_name: Zhiyuan full_name: Zeng, Zhiyuan last_name: Zeng - first_name: Tianyou full_name: Zhai, Tianyou last_name: Zhai - first_name: Han full_name: Zhang, Han last_name: Zhang - first_name: Hui full_name: Zhang, Hui last_name: Zhang - first_name: Qichun full_name: Zhang, Qichun last_name: Zhang - first_name: Tierui full_name: Zhang, Tierui last_name: Zhang - first_name: Xiang full_name: Zhang, Xiang last_name: Zhang - first_name: Li Dong full_name: Zhao, Li Dong last_name: Zhao - first_name: Meiting full_name: Zhao, Meiting last_name: Zhao - first_name: Weijie full_name: Zhao, Weijie last_name: Zhao - first_name: Yunxuan full_name: Zhao, Yunxuan last_name: Zhao - first_name: Kai Ge full_name: Zhou, Kai Ge last_name: Zhou - first_name: Xing full_name: Zhou, Xing last_name: Zhou - first_name: Yu full_name: Zhou, Yu last_name: Zhou - first_name: Hongwei full_name: Zhu, Hongwei last_name: Zhu - first_name: Hua full_name: Zhang, Hua last_name: Zhang - first_name: Zhongfan full_name: Liu, Zhongfan last_name: Liu citation: ama: Chang C, Chen W, Chen Y, et al. Recent progress on two-dimensional materials. Acta Physico-Chimica Sinica. 2021;37(12). doi:10.3866/PKU.WHXB202108017 apa: Chang, C., Chen, W., Chen, Y., Chen, Y., Chen, Y., Ding, F., … Liu, Z. (2021). Recent progress on two-dimensional materials. Acta Physico-Chimica Sinica. Peking University. https://doi.org/10.3866/PKU.WHXB202108017 chicago: Chang, Cheng, Wei Chen, Ye Chen, Yonghua Chen, Yu Chen, Feng Ding, Chunhai Fan, et al. “Recent Progress on Two-Dimensional Materials.” Acta Physico-Chimica Sinica. Peking University, 2021. https://doi.org/10.3866/PKU.WHXB202108017. ieee: C. Chang et al., “Recent progress on two-dimensional materials,” Acta Physico-Chimica Sinica, vol. 37, no. 12. Peking University, 2021. ista: Chang C, Chen W, Chen Y, Chen Y, Chen Y, Ding F, Fan C, Fan HJ, Fan Z, Gong C, Gong Y, He Q, Hong X, Hu S, Hu W, Huang W, Huang Y, Ji W, Li D, Li LJ, Li Q, Lin L, Ling C, Liu M, Liu N, Liu Z, Loh KP, Ma J, Miao F, Peng H, Shao M, Song L, Su S, Sun S, Tan C, Tang Z, Wang D, Wang H, Wang J, Wang X, Wang X, Wee ATS, Wei Z, Wu Y, Wu ZS, Xiong J, Xiong Q, Xu W, Yin P, Zeng H, Zeng Z, Zhai T, Zhang H, Zhang H, Zhang Q, Zhang T, Zhang X, Zhao LD, Zhao M, Zhao W, Zhao Y, Zhou KG, Zhou X, Zhou Y, Zhu H, Zhang H, Liu Z. 2021. Recent progress on two-dimensional materials. Acta Physico-Chimica Sinica. 37(12), 2108017. mla: Chang, Cheng, et al. “Recent Progress on Two-Dimensional Materials.” Acta Physico-Chimica Sinica, vol. 37, no. 12, 2108017, Peking University, 2021, doi:10.3866/PKU.WHXB202108017. short: C. Chang, W. Chen, Y. Chen, Y. Chen, Y. Chen, F. Ding, C. Fan, H.J. Fan, Z. Fan, C. Gong, Y. Gong, Q. He, X. Hong, S. Hu, W. Hu, W. Huang, Y. Huang, W. Ji, D. Li, L.J. Li, Q. Li, L. Lin, C. Ling, M. Liu, N. Liu, Z. Liu, K.P. Loh, J. Ma, F. Miao, H. Peng, M. Shao, L. Song, S. Su, S. Sun, C. Tan, Z. Tang, D. Wang, H. Wang, J. Wang, X. Wang, X. Wang, A.T.S. Wee, Z. Wei, Y. Wu, Z.S. Wu, J. Xiong, Q. Xiong, W. Xu, P. Yin, H. Zeng, Z. Zeng, T. Zhai, H. Zhang, H. Zhang, Q. Zhang, T. Zhang, X. Zhang, L.D. Zhao, M. Zhao, W. Zhao, Y. Zhao, K.G. Zhou, X. Zhou, Y. Zhou, H. Zhu, H. Zhang, Z. Liu, Acta Physico-Chimica Sinica 37 (2021). date_created: 2024-01-14T23:00:58Z date_published: 2021-10-13T00:00:00Z date_updated: 2024-01-17T11:29:33Z day: '13' department: - _id: MaIb doi: 10.3866/PKU.WHXB202108017 intvolume: ' 37' issue: '12' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.3866/PKU.WHXB202108017 month: '10' oa: 1 oa_version: Submitted Version publication: Acta Physico-Chimica Sinica publication_identifier: issn: - 1001-4861 publication_status: published publisher: Peking University quality_controlled: '1' scopus_import: '1' status: public title: Recent progress on two-dimensional materials type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2021' ... --- _id: '14889' abstract: - lang: eng text: We consider the Fröhlich Hamiltonian with large coupling constant α. For initial data of Pekar product form with coherent phonon field and with the electron minimizing the corresponding energy, we provide a norm approximation of the evolution, valid up to times of order α2. The approximation is given in terms of a Pekar product state, evolved through the Landau-Pekar equations, corrected by a Bogoliubov dynamics taking quantum fluctuations into account. This allows us to show that the Landau-Pekar equations approximately describe the evolution of the electron- and one-phonon reduced density matrices under the Fröhlich dynamics up to times of order α2. acknowledgement: "Financial support by the European Union’s Horizon 2020 research and innovation programme\r\nunder the Marie Skłodowska-Curie grant agreement No. 754411 (S.R.) and the European\r\nResearch Council under grant agreement No. 694227 (N.L. and R.S.), as well as by the SNSF\r\nEccellenza project PCEFP2 181153 (N.L.), the NCCR SwissMAP (N.L. and B.S.) and by the\r\nDeutsche Forschungsgemeinschaft (DFG) through the Research Training Group 1838: Spectral\r\nTheory and Dynamics of Quantum Systems (D.M.) is gratefully acknowledged. B.S. gratefully\r\nacknowledges financial support from the Swiss National Science Foundation through the Grant\r\n“Dynamical and energetic properties of Bose-Einstein condensates” and from the European\r\nResearch Council through the ERC-AdG CLaQS (grant agreement No 834782). D.M. thanks\r\nMarcel Griesemer for helpful discussions." article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Nikolai K full_name: Leopold, Nikolai K id: 4BC40BEC-F248-11E8-B48F-1D18A9856A87 last_name: Leopold orcid: 0000-0002-0495-6822 - first_name: David Johannes full_name: Mitrouskas, David Johannes id: cbddacee-2b11-11eb-a02e-a2e14d04e52d last_name: Mitrouskas - first_name: Simone Anna Elvira full_name: Rademacher, Simone Anna Elvira id: 856966FE-A408-11E9-977E-802DE6697425 last_name: Rademacher orcid: 0000-0001-5059-4466 - first_name: Benjamin full_name: Schlein, Benjamin last_name: Schlein - first_name: Robert full_name: Seiringer, Robert id: 4AFD0470-F248-11E8-B48F-1D18A9856A87 last_name: Seiringer orcid: 0000-0002-6781-0521 citation: ama: Leopold NK, Mitrouskas DJ, Rademacher SAE, Schlein B, Seiringer R. Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron. Pure and Applied Analysis. 2021;3(4):653-676. doi:10.2140/paa.2021.3.653 apa: Leopold, N. K., Mitrouskas, D. J., Rademacher, S. A. E., Schlein, B., & Seiringer, R. (2021). Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron. Pure and Applied Analysis. Mathematical Sciences Publishers. https://doi.org/10.2140/paa.2021.3.653 chicago: Leopold, Nikolai K, David Johannes Mitrouskas, Simone Anna Elvira Rademacher, Benjamin Schlein, and Robert Seiringer. “Landau–Pekar Equations and Quantum Fluctuations for the Dynamics of a Strongly Coupled Polaron.” Pure and Applied Analysis. Mathematical Sciences Publishers, 2021. https://doi.org/10.2140/paa.2021.3.653. ieee: N. K. Leopold, D. J. Mitrouskas, S. A. E. Rademacher, B. Schlein, and R. Seiringer, “Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron,” Pure and Applied Analysis, vol. 3, no. 4. Mathematical Sciences Publishers, pp. 653–676, 2021. ista: Leopold NK, Mitrouskas DJ, Rademacher SAE, Schlein B, Seiringer R. 2021. Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron. Pure and Applied Analysis. 3(4), 653–676. mla: Leopold, Nikolai K., et al. “Landau–Pekar Equations and Quantum Fluctuations for the Dynamics of a Strongly Coupled Polaron.” Pure and Applied Analysis, vol. 3, no. 4, Mathematical Sciences Publishers, 2021, pp. 653–76, doi:10.2140/paa.2021.3.653. short: N.K. Leopold, D.J. Mitrouskas, S.A.E. Rademacher, B. Schlein, R. Seiringer, Pure and Applied Analysis 3 (2021) 653–676. date_created: 2024-01-28T23:01:43Z date_published: 2021-10-01T00:00:00Z date_updated: 2024-02-05T10:02:45Z day: '01' department: - _id: RoSe doi: 10.2140/paa.2021.3.653 ec_funded: 1 external_id: arxiv: - '2005.02098' intvolume: ' 3' issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2005.02098 month: '10' oa: 1 oa_version: Preprint page: 653-676 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships - _id: 25C6DC12-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '694227' name: Analysis of quantum many-body systems publication: Pure and Applied Analysis publication_identifier: eissn: - 2578-5885 issn: - 2578-5893 publication_status: published publisher: Mathematical Sciences Publishers quality_controlled: '1' scopus_import: '1' status: public title: Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 3 year: '2021' ... --- _id: '14890' abstract: - lang: eng text: We consider a system of N interacting bosons in the mean-field scaling regime and construct corrections to the Bogoliubov dynamics that approximate the true N-body dynamics in norm to arbitrary precision. The N-independent corrections are given in terms of the solutions of the Bogoliubov and Hartree equations and satisfy a generalized form of Wick's theorem. We determine the n-point correlation functions of the excitations around the condensate, as well as the reduced densities of the N-body system, to arbitrary accuracy, given only the knowledge of the two-point functions of a quasi-free state and the solution of the Hartree equation. In this way, the complex problem of computing all n-point correlation functions for an interacting N-body system is essentially reduced to the problem of solving the Hartree equation and the PDEs for the Bogoliubov two-point functions. acknowledgement: "We are grateful for the hospitality of Central China Normal University (CCNU),\r\nwhere parts of this work were done, and thank Phan Th`anh Nam, Simone\r\nRademacher, Robert Seiringer and Stefan Teufel for helpful discussions. L.B. gratefully acknowledges the support by the German Research Foundation (DFG) within the Research\r\nTraining Group 1838 “Spectral Theory and Dynamics of Quantum Systems”, and the funding\r\nfrom the European Union’s Horizon 2020 research and innovation programme under the Marie\r\nSk lodowska-Curie Grant Agreement No. 754411." article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Lea full_name: Bossmann, Lea id: A2E3BCBE-5FCC-11E9-AA4B-76F3E5697425 last_name: Bossmann orcid: 0000-0002-6854-1343 - first_name: Sören P full_name: Petrat, Sören P id: 40AC02DC-F248-11E8-B48F-1D18A9856A87 last_name: Petrat orcid: 0000-0002-9166-5889 - first_name: Peter full_name: Pickl, Peter last_name: Pickl - first_name: Avy full_name: Soffer, Avy last_name: Soffer citation: ama: Bossmann L, Petrat SP, Pickl P, Soffer A. Beyond Bogoliubov dynamics. Pure and Applied Analysis. 2021;3(4):677-726. doi:10.2140/paa.2021.3.677 apa: Bossmann, L., Petrat, S. P., Pickl, P., & Soffer, A. (2021). Beyond Bogoliubov dynamics. Pure and Applied Analysis. Mathematical Sciences Publishers. https://doi.org/10.2140/paa.2021.3.677 chicago: Bossmann, Lea, Sören P Petrat, Peter Pickl, and Avy Soffer. “Beyond Bogoliubov Dynamics.” Pure and Applied Analysis. Mathematical Sciences Publishers, 2021. https://doi.org/10.2140/paa.2021.3.677. ieee: L. Bossmann, S. P. Petrat, P. Pickl, and A. Soffer, “Beyond Bogoliubov dynamics,” Pure and Applied Analysis, vol. 3, no. 4. Mathematical Sciences Publishers, pp. 677–726, 2021. ista: Bossmann L, Petrat SP, Pickl P, Soffer A. 2021. Beyond Bogoliubov dynamics. Pure and Applied Analysis. 3(4), 677–726. mla: Bossmann, Lea, et al. “Beyond Bogoliubov Dynamics.” Pure and Applied Analysis, vol. 3, no. 4, Mathematical Sciences Publishers, 2021, pp. 677–726, doi:10.2140/paa.2021.3.677. short: L. Bossmann, S.P. Petrat, P. Pickl, A. Soffer, Pure and Applied Analysis 3 (2021) 677–726. date_created: 2024-01-28T23:01:43Z date_published: 2021-10-01T00:00:00Z date_updated: 2024-02-05T09:26:31Z day: '01' department: - _id: RoSe doi: 10.2140/paa.2021.3.677 ec_funded: 1 external_id: arxiv: - '1912.11004' intvolume: ' 3' issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.1912.11004 month: '10' oa: 1 oa_version: Preprint page: 677-726 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Pure and Applied Analysis publication_identifier: eissn: - 2578-5885 issn: - 2578-5893 publication_status: published publisher: Mathematical Sciences Publishers quality_controlled: '1' scopus_import: '1' status: public title: Beyond Bogoliubov dynamics type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 3 year: '2021' ... --- _id: '14984' abstract: - lang: eng text: Hybrid zones are narrow geographic regions where different populations, races or interbreeding species meet and mate, producing mixed ‘hybrid’ offspring. They are relatively common and can be found in a diverse range of organisms and environments. The study of hybrid zones has played an important role in our understanding of the origin of species, with hybrid zones having been described as ‘natural laboratories’. This is because they allow us to study,in situ, the conditions and evolutionary forces that enable divergent taxa to remain distinct despite some ongoing gene exchange between them. article_processing_charge: No author: - first_name: Sean full_name: Stankowski, Sean id: 43161670-5719-11EA-8025-FABC3DDC885E last_name: Stankowski - first_name: Daria full_name: Shipilina, Daria id: 428A94B0-F248-11E8-B48F-1D18A9856A87 last_name: Shipilina orcid: 0000-0002-1145-9226 - first_name: Anja M full_name: Westram, Anja M id: 3C147470-F248-11E8-B48F-1D18A9856A87 last_name: Westram orcid: 0000-0003-1050-4969 citation: ama: 'Stankowski S, Shipilina D, Westram AM. Hybrid Zones. In: Encyclopedia of Life Sciences. Vol 2. eLS. Wiley; 2021. doi:10.1002/9780470015902.a0029355' apa: Stankowski, S., Shipilina, D., & Westram, A. M. (2021). Hybrid Zones. In Encyclopedia of Life Sciences (Vol. 2). Wiley. https://doi.org/10.1002/9780470015902.a0029355 chicago: Stankowski, Sean, Daria Shipilina, and Anja M Westram. “Hybrid Zones.” In Encyclopedia of Life Sciences, Vol. 2. ELS. Wiley, 2021. https://doi.org/10.1002/9780470015902.a0029355. ieee: S. Stankowski, D. Shipilina, and A. M. Westram, “Hybrid Zones,” in Encyclopedia of Life Sciences, vol. 2, Wiley, 2021. ista: 'Stankowski S, Shipilina D, Westram AM. 2021.Hybrid Zones. In: Encyclopedia of Life Sciences. vol. 2.' mla: Stankowski, Sean, et al. “Hybrid Zones.” Encyclopedia of Life Sciences, vol. 2, Wiley, 2021, doi:10.1002/9780470015902.a0029355. short: S. Stankowski, D. Shipilina, A.M. Westram, in:, Encyclopedia of Life Sciences, Wiley, 2021. date_created: 2024-02-14T12:05:50Z date_published: 2021-05-28T00:00:00Z date_updated: 2024-02-19T09:54:18Z day: '28' department: - _id: NiBa doi: 10.1002/9780470015902.a0029355 intvolume: ' 2' language: - iso: eng month: '05' oa_version: None publication: Encyclopedia of Life Sciences publication_identifier: eisbn: - '9780470015902' isbn: - '9780470016176' publication_status: published publisher: Wiley quality_controlled: '1' series_title: eLS status: public title: Hybrid Zones type: book_chapter user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2 year: '2021' ... --- _id: '14987' abstract: - lang: eng text: "The goal of zero-shot learning is to construct a classifier that can identify object classes for which no training examples are available. When training data for some of the object classes is available but not for others, the name generalized zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot is also used to describe other machine learning-based approaches that require no training data from the problem of interest, such as zero-shot action recognition or zero-shot machine translation." article_processing_charge: No author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. Computer Vision. 2nd ed. Cham: Springer; 2021:1395-1397. doi:10.1007/978-3-030-63416-2_874' apa: 'Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision (2nd ed., pp. 1395–1397). Cham: Springer. https://doi.org/10.1007/978-3-030-63416-2_874' chicago: 'Lampert, Christoph. “Zero-Shot Learning.” In Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63416-2_874.' ieee: 'C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.' ista: 'Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.' mla: Lampert, Christoph. “Zero-Shot Learning.” Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874. short: C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397. date_created: 2024-02-14T14:05:32Z date_published: 2021-10-13T00:00:00Z date_updated: 2024-02-19T10:59:04Z day: '13' department: - _id: ChLa doi: 10.1007/978-3-030-63416-2_874 edition: '2' editor: - first_name: Katsushi full_name: Ikeuchi, Katsushi last_name: Ikeuchi language: - iso: eng month: '10' oa_version: None page: 1395-1397 place: Cham publication: Computer Vision publication_identifier: eisbn: - '9783030634162' isbn: - '9783030634155' publication_status: published publisher: Springer quality_controlled: '1' status: public title: Zero-Shot Learning type: book_chapter user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14988' abstract: - lang: eng text: Raw data generated from the publication - The TPLATE complex mediates membrane bending during plant clathrin-mediated endocytosis by Johnson et al., 2021 In PNAS article_processing_charge: No author: - first_name: Alexander J full_name: Johnson, Alexander J id: 46A62C3A-F248-11E8-B48F-1D18A9856A87 last_name: Johnson orcid: 0000-0002-2739-8843 citation: ama: Johnson AJ. Raw data from Johnson et al, PNAS, 2021. 2021. doi:10.5281/ZENODO.5747100 apa: Johnson, A. J. (2021). Raw data from Johnson et al, PNAS, 2021. Zenodo. https://doi.org/10.5281/ZENODO.5747100 chicago: Johnson, Alexander J. “Raw Data from Johnson et Al, PNAS, 2021.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5747100. ieee: A. J. Johnson, “Raw data from Johnson et al, PNAS, 2021.” Zenodo, 2021. ista: Johnson AJ. 2021. Raw data from Johnson et al, PNAS, 2021, Zenodo, 10.5281/ZENODO.5747100. mla: Johnson, Alexander J. Raw Data from Johnson et Al, PNAS, 2021. Zenodo, 2021, doi:10.5281/ZENODO.5747100. short: A.J. Johnson, (2021). date_created: 2024-02-14T14:13:48Z date_published: 2021-12-01T00:00:00Z date_updated: 2024-02-19T11:06:09Z day: '01' ddc: - '580' department: - _id: JiFr doi: 10.5281/ZENODO.5747100 has_accepted_license: '1' main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.5747100 month: '12' oa: 1 oa_version: Published Version publisher: Zenodo related_material: record: - id: '9887' relation: used_in_publication status: public status: public title: Raw data from Johnson et al, PNAS, 2021 tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ...