Papers
Asynchronous Distributed Variational Gaussian Process for Regression
Hao Peng, Shandian Zhe, Xiao Zhang et al.
Curiosity-driven Exploration by Self-supervised Prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros et al.
Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery
Ashkan Panahi, Devdatt Dubhashi, Fredrik D. Johansson et al.
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
Yunpeng Pan, Xinyan Yan, Evangelos A. Theodorou et al.
A Birth-Death Process for Feature Allocation
Konstantina Palla, David Knowles, Zoubin Ghahramani
Stochastic Bouncy Particle Sampler
Ari Pakman, Dar Gilboa, David Carlson et al.
Dictionary Learning Based on Sparse Distribution Tomography
Pedram Pad, Farnood Salehi, Elisa Celis et al.
Count-Based Exploration with Neural Density Models
Georg Ostrovski, Marc G. Bellemare, Aäron Oord et al.
Bidirectional Learning for Time-series Models with Hidden Units
Takayuki Osogami, Hiroshi Kajino, Taro Sekiyama
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Ian Osband, Benjamin Van Roy
Algebraic Variety Models for High-Rank Matrix Completion
Greg Ongie, Rebecca Willett, Robert D. Nowak et al.
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Shayegan Omidshafiei, Jason Pazis, Christopher Amato et al.
The Statistical Recurrent Unit
Junier B. Oliva, Barnabás Póczos, Jeff Schneider
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Junhyuk Oh, Satinder Singh, Honglak Lee et al.
Nyström Method with Kernel K-means++ Samples as Landmarks
Dino Oglic, Thomas Gärtner
Conditional Image Synthesis with Auxiliary Classifier GANs
Augustus Odena, Christopher Olah, Jonathon Shlens
Multichannel End-to-end Speech Recognition
Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori et al.
Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data
Xiuyan Ni, Novi Quadrianto, Yusu Wang et al.
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Lam M. Nguyen, Jie Liu, Katya Scheinberg et al.
The Loss Surface of Deep and Wide Neural Networks
Quynh Nguyen, Matthias Hein
Post-Inference Prior Swapping
Willie Neiswanger, Eric Xing
Delta Networks for Optimized Recurrent Network Computation
Daniel Neil, Jun Haeng Lee, Tobi Delbruck et al.
Adaptive Sampling Probabilities for Non-Smooth Optimization
Hongseok Namkoong, Aman Sinha, Steve Yadlowsky et al.
Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition
Tasha Nagamine, Nima Mesgarani
Meta Networks
Tsendsuren Munkhdalai, Hong Yu