Papers
Structure-informed Language Models Are Protein Designers
Zaixiang Zheng, Yifan Deng, Dongyu Xue et al.
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
Axel Sauer, Tero Karras, Samuli Laine et al.
Subequivariant Graph Reinforcement Learning in 3D Environments
Runfa Chen, Jiaqi Han, Fuchun Sun et al.
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Jin-Hong Du, Pratik Patil, Arun K. Kuchibhotla
Subset-Based Instance Optimality in Private Estimation
Travis Dick, Alex Kulesza, Ziteng Sun et al.
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
Niclas Boehmer, L. Elisa Celis, Lingxiao Huang et al.
Superhuman Fairness
Omid Memarrast, Linh Vu, Brian D Ziebart
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Supported Trust Region Optimization for Offline Reinforcement Learning
Yixiu Mao, Hongchang Zhang, Chen Chen et al.
SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems
Aaron M Ferber, Taoan Huang, Daochen Zha et al.
Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction
Yuan-Ting Hu, Alex Schwing, Raymond A. Yeh
SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes
Junetae Kim, Kyoungsuk Park, Hanseok Jeong et al.
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
Junyi Zhu, Ruicong Yao, Matthew B. Blaschko
Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks
Shikuang Deng, Hao Lin, Yuhang Li et al.
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Max Ryabinin, Tim Dettmers, Michael Diskin et al.
Symmetry-Aware Robot Design with Structured Subgroups
Heng Dong, Junyu Zhang, Tonghan Wang et al.
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning
Sebastien Lachapelle, Tristan Deleu, Divyat Mahajan et al.
Synthetic data for model selection
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky et al.
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data
Boris Van Breugel, Zhaozhi Qian, Mihaela Van Der Schaar
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Zhihong Shao, Yeyun Gong, Yelong Shen et al.
System Identification of Neural Systems: If We Got It Right, Would We Know?
Yena Han, Tomaso A Poggio, Brian Cheung
TabDDPM: Modelling Tabular Data with Diffusion Models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev et al.
TabLeak: Tabular Data Leakage in Federated Learning
Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov et al.