Identification of neural oscillations and epileptiform changes in human brain organoids
R.A. Samarasinghe, O. Miranda, J.E. Buth, S. Mitchell, I. Ferando, M. Watanabe, A. Kurdian, P. Golshani, K. Plath, W.E. Lowry, J.M. Parent, I. Mody, B.G. Novitch, Identification of Neural Oscillations and Epileptiform Changes in Human Brain Organoids, Springer Nature, 2021.

Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches
F. Lombardi, O. Shriki, H.J. Herrmann, L. de Arcangelis, Neurocomputing 461 (2021) 657–666.

Attosecond interferometry of shape resonances in the recoil frame of C
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All-optical probe of three-dimensional topological insulators based on high-harmonic generation by circularly polarized laser fields
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Strong-field physics in three-dimensional topological insulators
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Science with the ultraviolet explorer (UVEX)
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Toward causal representation learning
B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.

Neighborhood contrastive learning applied to online patient monitoring
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.

On disentangled representations learned from correlated data
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.

On the transfer of disentangled representations in realistic settings
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.

Self-supervised learning with data augmentations provably isolates content from style
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.

Dynamic inference with neural interpreters
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.

Boosting variational inference with locally adaptive step-sizes
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Backward-compatible prediction updates: A probabilistic approach
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.

Enforcing and discovering structure in machine learning
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Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse
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Representation learning for out-of-distribution generalization in reinforcement learning
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Recent progress on two-dimensional materials
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).

Landau–Pekar equations and quantum fluctuations for the dynamics of a strongly coupled polaron
N.K. Leopold, D.J. Mitrouskas, S.A.E. Rademacher, B. Schlein, R. Seiringer, Pure and Applied Analysis 3 (2021) 653–676.

Beyond Bogoliubov dynamics
L. Bossmann, S.P. Petrat, P. Pickl, A. Soffer, Pure and Applied Analysis 3 (2021) 677–726.