Papers
Improved Parallel Algorithms for Density-Based Network Clustering
Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrović
Learning and Data Selection in Big Datasets
Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione et al.
Partially Linear Additive Gaussian Graphical Models
Sinong Geng, Minhao Yan, Mladen Kolar et al.
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman et al.
A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, Olivier Pietquin
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman, Ran El-Yaniv
Optimal Mini-Batch and Step Sizes for SAGA
Nidham Gazagnadou, Robert Gower, Joseph Salmon
Multi-Frequency Phase Synchronization
Tingran Gao, Zhizhen Zhao
Geometric Scattering for Graph Data Analysis
Feng Gao, Guy Wolf, Matthew Hirn
Demystifying Dropout
Hongchang Gao, Jian Pei, Heng Huang
Rate Distortion For Model Compression:From Theory To Practice
Weihao Gao, Yu-Han Liu, Chong Wang et al.
Deep Generative Learning via Variational Gradient Flow
Yuan Gao, Yuling Jiao, Yang Wang et al.
Graph U-Nets
Hongyang Gao, Shuiwang Ji
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
Octavian Ganea, Sylvain Gelly, Gary Becigneul et al.
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
Shani Gamrian, Yoav Goldberg
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto, David Meger, Doina Precup
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
Kaito Fujii, Shinsaku Sakaue
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
Szu-Wei Fu, Chien-Feng Liao, Yu Tsao et al.
Diagnosing Bottlenecks in Deep Q-learning Algorithms
Justin Fu, Aviral Kumar, Matthew Soh et al.
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton
Fast and Flexible Inference of Joint Distributions from their Marginals
Charlie Frogner, Tomaso Poggio
Approximating Orthogonal Matrices with Effective Givens Factorization
Thomas Frerix, Joan Bruna
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
Dror Freirich, Tzahi Shimkin, Ron Meir et al.
Learning Discrete Structures for Graph Neural Networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil et al.
On discriminative learning of prediction uncertainty
Vojtech Franc, Daniel Prusa