Papers
Conditional Noise-Contrastive Estimation of Unnormalised Models
Ciwan Ceylan, Michael U. Gutmann
Fair and Diverse DPP-Based Data Summarization
Elisa Celis, Vijay Keswani, Damian Straszak et al.
Adversarial Learning with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu et al.
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Trevor Campbell, Tamara Broderick
Improved large-scale graph learning through ridge spectral sparsification
Daniele Calandriello, Alessandro Lazaric, Ioannis Koutis et al.
Path-Level Network Transformation for Efficient Architecture Search
Han Cai, Jiacheng Yang, Weinan Zhang et al.
Quasi-Monte Carlo Variational Inference
Alexander Buchholz, Florian Wenzel, Stephan Mandt
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim, Amir Globerson
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
Vladimir Braverman, Stephen Chestnut, Robert Krauthgamer et al.
QuantTree: Histograms for Change Detection in Multivariate Data Streams
Giacomo Boracchi, Diego Carrera, Cristiano Cervellera et al.
Prediction Rule Reshaping
Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber et al.
A Progressive Batching L-BFGS Method for Machine Learning
Raghu Bollapragada, Jorge Nocedal, Dheevatsa Mudigere et al.
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner et al.
Optimizing the Latent Space of Generative Networks
Piotr Bojanowski, Armand Joulin, David Lopez-Pas et al.
Adaptive Sampled Softmax with Kernel Based Sampling
Guy Blanc, Steffen Rendle
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Mikolaj Binkowski, Gautier Marti, Philippe Donnat
Distributed Clustering via LSH Based Data Partitioning
Aditya Bhaskara, Maheshakya Wijewardena
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli et al.
Understanding and Simplifying One-Shot Architecture Search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph et al.
To Understand Deep Learning We Need to Understand Kernel Learning
Mikhail Belkin, Siyuan Ma, Soumik Mandal
Mutual Information Neural Estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar et al.
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
Peter Bartlett, Dave Helmbold, Philip Long
Measuring abstract reasoning in neural networks
David Barrett, Felix Hill, Adam Santoro et al.
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Andre Barreto, Diana Borsa, John Quan et al.
Testing Sparsity over Known and Unknown Bases
Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal