Papers
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Chirag Gupta, Arun Sai Suggala, Ankit Goyal et al.
On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun et al.
Automated Curriculum Learning for Neural Networks
Alex Graves, Marc G. Bellemare, Jacob Menick et al.
Efficient softmax approximation for GPUs
Grave, Armand Joulin, Moustapha Cissé et al.
Measuring Sample Quality with Kernels
Jackson Gorham, Lester Mackey
Preferential Bayesian Optimization
Javier González, Zhenwen Dai, Andreas Damianou et al.
Convex Phase Retrieval without Lifting via PhaseMax
Tom Goldstein, Christoph Studer
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley et al.
On Context-Dependent Clustering of Bandits
Claudio Gentile, Shuai Li, Purushottam Kar et al.
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier et al.
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis
Rong Ge, Chi Jin, Yi Zheng
Zonotope Hit-and-run for Efficient Sampling from Projection DPPs
Guillaume Gautier, Rémi Bardenet, Michal Valko
Differentiable Programs with Neural Libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman et al.
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis
Dan Garber, Ohad Shamir, Nathan Srebro
Local-to-Global Bayesian Network Structure Learning
Tian Gao, Kshitij Fadnis, Murray Campbell
Deep Bayesian Active Learning with Image Data
Yarin Gal, Riashat Islam, Zoubin Ghahramani
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Joseph Futoma, Sanjay Hariharan, Katherine Heller
Forward and Reverse Gradient-Based Hyperparameter Optimization
Luca Franceschi, Michele Donini, Paolo Frasconi et al.
Counterfactual Data-Fusion for Online Reinforcement Learners
Andrew Forney, Judea Pearl, Elias Bareinboim
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar et al.
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein et al.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, Sergey Levine
Coresets for Vector Summarization with Applications to Network Graphs
Dan Feldman, Sedat Ozer, Daniela Rus
Regret Minimization in Behaviorally-Constrained Zero-Sum Games
Gabriele Farina, Christian Kroer, Tuomas Sandholm
Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye et al.