_id,doi,title
14428,10.1007/978-3-031-38545-2_17,"Random oracle combiners: Breaking the concatenation barrier for collision-resistance"
14441,10.1007/s00220-023-04841-3,The Fröhlich Polaron at strong coupling: Part I - The quantum correction to the classical energy
14444,10.1007/s11856-023-2513-9,Substructures in Latin squares
14445,10.1007/s11856-023-2521-9,"Coboundary expansion, equivariant overlap, and crossing numbers of simplicial complexes"
14446,10.2478/msr-2023-0023,Against the flow of time with multi-output models
14447,10.1007/s10725-023-01083-0,New fluorescent auxin derivatives: anti-auxin activity and accumulation patterns in Arabidopsis thaliana
14448,10.1109/CVPR52729.2023.01153,Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions
14449,10.3389/fmicb.2023.1257002,Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action
14451,10.1007/s00521-023-09033-7,Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading
14452,10.1093/genetics/iyad133,The infinitesimal model with dominance
14453,10.1029/2022MS003477,Extreme precipitation in tropical squall lines
14454,10.1007/978-3-031-44267-4_15,Monitoring algorithmic fairness under partial observations
14455,10.3389/fpsyt.2023.1287879,Tempering expectations: Considerations on the current state of stem cells therapy for autism treatment
14456,10.1007/978-3-031-43587-4_24,Shortest dominating set reconfiguration under token sliding
14457,10.1007/978-3-031-44469-2_11,"Stronger lower bounds for leakage-resilient secret sharing"
14458,,SparseGPT: Massive language models can be accurately pruned in one-shot
14459,,"Fundamental limits of two-layer autoencoders, and achieving them with gradient methods"
14460,,SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge
14461,,Quantized distributed training of large models with convergence guarantees
14462,,Constant matters: Fine-grained error bound on differentially private continual observation