Papers
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer et al.
Going From Image to Video Saliency: Augmenting Image Salience With Dynamic Attentional Push
Siavash Gorji, James J. Clark
Gold Corpus for Telegraphic Summarization
Chanakya Malireddy, Srivenkata N M Somisetty, Manish Shrivastava
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions
Lori Moon, Christos Christodoulopoulos, Cynthia Fisher et al.
Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy
Jiasen Yang, Qiang Liu, Vinayak Rao et al.
Good View Hunting: Learning Photo Composition From Dense View Pairs
Zijun Wei, Jianming Zhang, Xiaohui Shen et al.
GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning
Jacky Liang, Viktor Makoviychuk, Ankur Handa et al.
GPU-Based Max Flow Maps in the Plane
Renato Farias, Marcelo Kallmann
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger 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 for Spiking Neural Networks
Dongsung Huh, Terrence J. Sejnowski
Gradient Descent Learns Linear Dynamical Systems
Moritz Hardt, Tengyu Ma, Benjamin Recht
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 Diversity: a Key Ingredient for Scalable Distributed Learning
Dong Yin, Ashwin Pananjady, Max Lam et al.
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing
Vivek S. Borkar, Vikranth R. Dwaracherla, Neeraja Sahasrabudhe
Gradient Estimators for Implicit Models
Yingzhen Li, Richard E. Turner
Gradient Hard Thresholding Pursuit
Xiao-Tong Yuan, Ping Li, Tong Zhang
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
Atsushi Nitanda, Taiji Suzuki
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Mingyi Hong, Meisam Razaviyayn, Jason Lee
Gradient Sparsification for Communication-Efficient Distributed Optimization
Jianqiao Wangni, Jialei Wang, Ji Liu et al.