Papers
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
Daniel Sheldon, Kevin Winner, Debora Sujono
Locally Private Hypothesis Testing
Or Sheffet
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer, Mitchell Stern
Solving Partial Assignment Problems using Random Clique Complexes
Charu Sharma, Deepak Nathani, Manohar Kaul
Finding Influential Training Samples for Gradient Boosted Decision Trees
Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov et al.
First Order Generative Adversarial Networks
Calvin Seward, Thomas Unterthiner, Urs Bergmann et al.
Bounding and Counting Linear Regions of Deep Neural Networks
Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam
Overcoming Catastrophic Forgetting with Hard Attention to the Task
Joan Serra, Didac Suris, Marius Miron et al.
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina et al.
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patrick Schwab, Emanuela Keller, Carl Muroi et al.
Learning with Abandonment
Sven Schmit, Ramesh Johari
Tight Regret Bounds for Bayesian Optimization in One Dimension
Jonathan Scarlett
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Amartya Sanyal, Matt Kusner, Adria Gascon et al.
A Classification-Based Study of Covariate Shift in GAN Distributions
Shibani Santurkar, Ludwig Schmidt, Aleksander Madry
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg et al.
Representation Tradeoffs for Hyperbolic Embeddings
Frederic Sala, Chris De Sa, Albert Gu et al.
Tempered Adversarial Networks
Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou et al.
Learning Equations for Extrapolation and Control
Subham Sahoo, Christoph Lampert, Georg Martius
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Itay Safran, Ohad Shamir
Black-Box Variational Inference for Stochastic Differential Equations
Tom Ryder, Andrew Golightly, A. Stephen McGough et al.
Probabilistic Boolean Tensor Decomposition
Tammo Rukat, Chris Holmes, Christopher Yau
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Francisco Ruiz, Michalis Titsias, Adji Bousso Dieng et al.
Deep One-Class Classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz et al.
Fast Information-theoretic Bayesian Optimisation
Binxin Ru, Michael A. Osborne, Mark Mcleod et al.