Papers
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments
Dane S Corneil, Wulfram Gerstner
A Universal Catalyst for First-Order Optimization
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
A Universal Primal-Dual Convex Optimization Framework
Alp Yurtsever, Quoc Tran Dinh, Volkan Cevher
Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman et al.
Backpropagation for Energy-Efficient Neuromorphic Computing
Steve K Esser, Rathinakumar Appuswamy, Paul Merolla et al.
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions
Dominik Rothenhäusler, Christina Heinze, Jonas Peters et al.
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
Ofer Dekel, Ronen Eldan, Tomer Koren
Bandits with Unobserved Confounders: A Causal Approach
Elias Bareinboim, Andrew Forney, Judea Pearl
Barrier Frank-Wolfe for Marginal Inference
Rahul G Krishnan, Simon Lacoste-Julien, David Sontag
Basis refinement strategies for linear value function approximation in MDPs
Gheorghe Comanici, Doina Precup, Prakash Panangaden
Bayesian Active Model Selection with an Application to Automated Audiometry
Jacob Gardner, Gustavo Malkomes, Roman Garnett et al.
Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy et al.
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar et al.
Bayesian Optimization with Exponential Convergence
Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
b-bit Marginal Regression
Martin Slawski, Ping Li
Beyond Convexity: Stochastic Quasi-Convex Optimization
Elad Hazan, Kfir Levy, Shai Shalev-Shwartz
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
Vidyashankar Sivakumar, Arindam Banerjee, Pradeep K Ravikumar
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
Yan Huang, Wei Wang, Liang Wang
Bidirectional Recurrent Neural Networks as Generative Models
Mathias Berglund, Tapani Raiko, Mikko Honkala et al.
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Biologically Inspired Dynamic Textures for Probing Motion Perception
Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet et al.
Black-box optimization of noisy functions with unknown smoothness
Jean-Bastien Grill, Michal Valko, Remi Munos et al.
Bounding errors of Expectation-Propagation
Guillaume P Dehaene, Simon Barthelmé
Bounding the Cost of Search-Based Lifted Inference
David B Smith, Vibhav G Gogate
Calibrated Structured Prediction
Volodymyr Kuleshov, Percy Liang