Papers
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Simple strategies for recovering inner products from coarsely quantized random projections
Ping Li, Martin Slawski
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
Ahmet Alacaoglu, Quoc Tran Dinh, Olivier Fercoq et al.
Sobolev Training for Neural Networks
Wojciech M. Czarnecki, Simon Osindero, Max Jaderberg et al.
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen et al.
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities
Michael Eickenberg, Georgios Exarchakis, Matthew Hirn et al.
Solving Most Systems of Random Quadratic Equations
Gang Wang, Georgios Giannakis, Yousef Saad et al.
Sparse Approximate Conic Hulls
Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi
Sparse convolutional coding for neuronal assembly detection
Sven Peter, Elke Kirschbaum, Martin Both et al.
Sparse Embedded $k$-Means Clustering
Weiwei Liu, Xiaobo Shen, Ivor Tsang
Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, Matus J Telgarsky
Spectral Mixture Kernels for Multi-Output Gaussian Processes
Gabriel Parra, Felipe Tobar
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
Pan Xu, Jian Ma, Quanquan Gu
Spherical convolutions and their application in molecular modelling
Wouter Boomsma, Jes Frellsen
Stabilizing Training of Generative Adversarial Networks through Regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin et al.
State Aware Imitation Learning
Yannick Schroecker, Charles L Isbell
Statistical Cost Sharing
Eric Balkanski, Umar Syed, Sergei Vassilvitskii
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud
Stochastic and Adversarial Online Learning without Hyperparameters
Ashok Cutkosky, Kwabena A. Boahen
Stochastic Approximation for Canonical Correlation Analysis
Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy et al.
Stochastic Mirror Descent in Variationally Coherent Optimization Problems
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos et al.
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure
Alberto Bietti, Julien Mairal
Stochastic Submodular Maximization: The Case of Coverage Functions
Mohammad Karimi, Mario Lucic, Hamed Hassani et al.
Straggler Mitigation in Distributed Optimization Through Data Encoding
Can Karakus, Yifan Sun, Suhas Diggavi et al.