Papers
11,951 papers found
Not-So-Random Features
Brian Bullins, Cyril Zhang, Yi Zhang
Online Learning Rate Adaptation with Hypergradient Descent
Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio et al.
On the Convergence of Adam and Beyond
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
On the Discrimination-Generalization Tradeoff in GANs
Pengchuan Zhang, Qiang Liu, Dengyong Zhou et al.
On the Expressive Power of Overlapping Architectures of Deep Learning
Or Sharir, Amnon Shashua
On the importance of single directions for generalization
Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz et al.
On the Information Bottleneck Theory of Deep Learning
Andrew Michael Saxe, Yamini Bansal, Joel Dapello et al.
On the insufficiency of existing momentum schemes for Stochastic Optimization
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain et al.
On the regularization of Wasserstein GANs
Henning Petzka, Asja Fischer, Denis Lukovnikov
On the State of the Art of Evaluation in Neural Language Models
Gábor Melis, Chris Dyer, Phil Blunsom
On Unifying Deep Generative Models
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov et al.
On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach
Sriraam Natarajan, Srijita Das, Nandini Ramanan et al.
Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation
Xu He, Herbert Jaeger
Pairwise Liquid Democracy
Markus Brill, Nimrod Talmon
Parallelizing Linear Recurrent Neural Nets Over Sequence Length
Eric Martin, Chris Cundy
Parameter Space Noise for Exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal et al.
Parametrized Hierarchical Procedures for Neural Programming
Roy Fox, Richard Shin, Sanjay Krishnan et al.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Yang Song, Taesup Kim, Sebastian Nowozin et al.
PixelNN: Example-based Image Synthesis
Aayush Bansal, Yaser Sheikh, Deva Ramanan
Polar Transformer Networks
Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou et al.
Policy Optimization by Genetic Distillation
Tanmay Gangwani, Jian Peng
Positive and Unlabeled Learning via Loss Decomposition and Centroid Estimation
Hong Shi, Shaojun Pan, Jian Yang et al.
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
William Falcon, Henning Schulzrinne
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras, Timo Aila, Samuli Laine et al.
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Glen Berseth, Cheng Xie, Paul Cernek et al.