Papers
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Junnan Li, Dongxu Li, Caiming Xiong et al.
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David et al.
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
Namuk Park, Songkuk Kim
Boosting Graph Structure Learning with Dummy Nodes
Xin Liu, Jiayang Cheng, Yangqiu Song et al.
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
Giuseppe Bruno De Luca, Eva Silverstein
Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis
Alexander Munteanu, Simon Omlor, Zhao Song et al.
Bounding Training Data Reconstruction in Private (Deep) Learning
Chuan Guo, Brian Karrer, Kamalika Chaudhuri et al.
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
Yifan Peng, Siddharth Dalmia, Ian Lane et al.
Branching Reinforcement Learning
Yihan Du, Wei Chen
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Julian Bitterwolf, Alexander Meinke, Maximilian Augustin et al.
Breaking the $\sqrtT$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
Avishek Ghosh, Abishek Sankararaman
Bregman Neural Networks
Jordan Frecon, Gilles Gasso, Massimiliano Pontil et al.
Bregman Power k-Means for Clustering Exponential Family Data
Adithya Vellal, Saptarshi Chakraborty, Jason Q Xu
Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes
Tim Tsz-Kit Lau, Han Liu
Building Robust Ensembles via Margin Boosting
Dinghuai Zhang, Hongyang Zhang, Aaron Courville et al.
Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning
Cristiano Capone, Cosimo Lupo, Paolo Muratore et al.
ButterflyFlow: Building Invertible Layers with Butterfly Matrices
Chenlin Meng, Linqi Zhou, Kristy Choi et al.
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta et al.
C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra
Yuka Hashimoto, Zhao Wang, Tomoko Matsui
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
Volodymyr Kuleshov, Shachi Deshpande
Calibrated Learning to Defer with One-vs-All Classifiers
Rajeev Verma, Eric Nalisnick
Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning
Yingjie Fei, Ruitu Xu
Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff et al.
Causal Dynamics Learning for Task-Independent State Abstraction
Zizhao Wang, Xuesu Xiao, Zifan Xu et al.
Causal Imitation Learning under Temporally Correlated Noise
Gokul Swamy, Sanjiban Choudhury, Drew Bagnell et al.