Papers
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Elvis Dohmatob et al.
Scalable Spike-and-Slab
Niloy Biswas, Lester Mackey, Xiao-Li Meng
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric et al.
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika et al.
Scaling Structured Inference with Randomization
Yao Fu, John Cunningham, Mirella Lapata
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework
Jiahao Su, Wonmin Byeon, Furong Huang
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
Giorgio Giannone, Ole Winther
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Jaehyeong Jo, Seul Lee, Sung Ju Hwang
Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems
Giannis Daras, Yuval Dagan, Alex Dimakis et al.
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Paul Rolland, Volkan Cevher, Matthäus Kleindessner et al.
SDQ: Stochastic Differentiable Quantization with Mixed Precision
Xijie Huang, Zhiqiang Shen, Shichao Li et al.
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du et al.
Searching for BurgerFormer with Micro-Meso-Macro Space Design
Longxing Yang, Yu Hu, Shun Lu et al.
Secure Distributed Training at Scale
Eduard Gorbunov, Alexander Borzunov, Michael Diskin et al.
Secure Quantized Training for Deep Learning
Marcel Keller, Ke Sun
Selective Network Linearization for Efficient Private Inference
Minsu Cho, Ameya Joshi, Brandon Reagen et al.
Selective Regression under Fairness Criteria
Abhin Shah, Yuheng Bu, Joshua K Lee et al.
Self-conditioning Pre-Trained Language Models
Xavier Suau Cuadros, Luca Zappella, Nicholas Apostoloff
Self-Organized Polynomial-Time Coordination Graphs
Qianlan Yang, Weijun Dong, Zhizhou Ren et al.
Self-supervised learning with random-projection quantizer for speech recognition
Chung-Cheng Chiu, James Qin, Yu Zhang et al.
Self-supervised Models are Good Teaching Assistants for Vision Transformers
Haiyan Wu, Yuting Gao, Yinqi Zhang et al.
Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech
Aditya R Vaidya, Shailee Jain, Alexander Huth
Self-Supervised Representation Learning via Latent Graph Prediction
Yaochen Xie, Zhao Xu, Shuiwang Ji
Selling Data To a Machine Learner: Pricing via Costly Signaling
Junjie Chen, Minming Li, Haifeng Xu
Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound
Shion Takeno, Tomoyuki Tamura, Kazuki Shitara et al.