Papers
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
Regularized Nonlinear Acceleration
Damien Scieur, Alexandre d'Aspremont, Francis Bach
Relevant sparse codes with variational information bottleneck
Matthew Chalk, Olivier Marre, Gasper Tkacik
Rényi Divergence Variational Inference
Yingzhen Li, Richard E Turner
Reshaped Wirtinger Flow for Solving Quadratic System of Equations
Huishuai Zhang, Yingbin Liang
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Andreas Veit, Michael J Wilber, Serge Belongie
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun et al.
Review Networks for Caption Generation
Zhilin Yang, Ye Yuan, Yuexin Wu et al.
Reward Augmented Maximum Likelihood for Neural Structured Prediction
Mohammad Norouzi, Samy Bengio, zhifeng Chen et al.
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Jifeng Dai, Yi Li, Kaiming He et al.
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds
Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
Robust k-means: a Theoretical Revisit
ALEXANDROS GEORGOGIANNIS
Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Safe and Efficient Off-Policy Reinforcement Learning
Remi Munos, Tom Stepleton, Anna Harutyunyan et al.
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Matteo Turchetta, Felix Berkenkamp, Andreas Krause
Safe Policy Improvement by Minimizing Robust Baseline Regret
Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow
Sample Complexity of Automated Mechanism Design
Maria-Florina F Balcan, Tuomas Sandholm, Ellen Vitercik
Sampling for Bayesian Program Learning
Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh, Andrew Cotter, Maya Gupta et al.
Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher et al.
Scaled Least Squares Estimator for GLMs in Large-Scale Problems
Murat A Erdogdu, Lee H Dicker, Mohsen Bayati
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Yin Cheng Ng, Pawel M Chilinski, Ricardo Silva
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Jack Rae, Jonathan J Hunt, Ivo Danihelka et al.
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
Bryan D He, Christopher M De Sa, Ioannis Mitliagkas et al.