Papers
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Eugenio Bargiacchi, Timothy Verstraeten, Diederik Roijers et al.
Geodesic Convolutional Shape Optimization
Pierre Baque, Edoardo Remelli, Francois Fleuret et al.
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista, Matthias Poloczek
Classification from Pairwise Similarity and Unlabeled Data
Han Bao, Gang Niu, Masashi Sugiyama
Using Inherent Structures to design Lean 2-layer RBMs
Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya
Improved Training of Generative Adversarial Networks Using Representative Features
Duhyeon Bang, Hyunjung Shim
Improving Optimization for Models With Continuous Symmetry Breaking
Robert Bamler, Stephan Mandt
Differentially Private Database Release via Kernel Mean Embeddings
Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Lukas Balles, Philipp Hennig
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Borja Balle, Yu-Xiang Wang
Approximation Guarantees for Adaptive Sampling
Eric Balkanski, Yaron Singer
A Spline Theory of Deep Learning
Randall Balestriero, baraniuk
Spline Filters For End-to-End Deep Learning
Randall Balestriero, Romain Cosentino, Herve Glotin et al.
The Mechanics of n-Player Differentiable Games
David Balduzzi, Sebastien Racaniere, James Martens et al.
Learning to Branch
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm et al.
A Boo(n) for Evaluating Architecture Performance
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Chandrajit Bajaj, Tingran Gao, Zihang He et al.
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Marco Baity-Jesi, Levent Sagun, Mario Geiger et al.
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Wenruo Bai, Jeff Bilmes
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Davide Bacciu, Federico Errica, Alessio Micheli
Synthesizing Robust Adversarial Examples
Anish Athalye, Logan Engstrom, Andrew Ilyas et al.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, David Wagner
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi, Dipendra Misra, Michael Littman
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur et al.
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan