Papers
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Maryam Fazel, Rong Ge, Sham Kakade et al.
Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy
Jiasen Yang, Qiang Liu, Vinayak Rao et al.
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Yoonho Lee, Seungjin Choi
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Netanel Raviv, Rashish Tandon, Alex Dimakis et al.
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Yao Ma, Alexander Olshevsky, Csaba Szepesvari et al.
Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima
Simon Du, Jason Lee, Yuandong Tian et al.
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
Peter Bartlett, Dave Helmbold, Philip Long
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Mingyi Hong, Meisam Razaviyayn, Jason Lee
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee et al.
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao, Zhishuai Zhang, Wei Shen et al.
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Qiang Sun, Kean Ming Tan, Han Liu et al.
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg et al.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You, Rex Ying, Xiang Ren et al.
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Wenruo Bai, Jeff Bilmes
Hierarchical Clustering with Structural Constraints
Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Zhengping Che, Sanjay Purushotham, Guangyu Li et al.
Hierarchical Imitation and Reinforcement Learning
Hoang Le, Nan Jiang, Alekh Agarwal et al.
Hierarchical Long-term Video Prediction without Supervision
Nevan wichers, Ruben Villegas, Dumitru Erhan et al.
Hierarchical Multi-Label Classification Networks
Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros
Hierarchical Text Generation and Planning for Strategic Dialogue
Denis Yarats, Mike Lewis
High Performance Zero-Memory Overhead Direct Convolutions
Jiyuan Zhang, Franz Franchetti, Tze Meng Low
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Tim Pearce, Alexandra Brintrup, Mohamed Zaki et al.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian Ganea, Gary Becigneul, Thomas Hofmann
Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit et al.