34 Results

Tutorial
Mon 8:00 Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
Andrew Wilson
AffinityWorkshop
Mon 11:35 Breakout Session 4.9: Uncertainty Estimation in Bayesian Deep Learning
Poster
Tue 7:00 Variational Bayesian Quantization
Yibo Yang, Robert Bamler, Stephan Mandt
Poster
Tue 7:00 A Free-Energy Principle for Representation Learning
Yansong Gao, Pratik Chaudhari
Poster
Tue 8:00 NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian
Poster
Tue 9:00 Being Bayesian about Categorical Probability
Taejong Joo, Uijung Chung, Min-Gwan Seo
Poster
Tue 9:00 Disentangling Trainability and Generalization in Deep Neural Networks
Lechao Xiao, Jeffrey Pennington, Samuel Schoenholz
Poster
Tue 9:00 Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian
Poster
Tue 9:00 Variance Reduction in Stochastic Particle-Optimization Sampling
Jianyi Zhang, Yang Zhao, Changyou Chen
Poster
Tue 9:00 How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
Poster
Tue 12:00 Bayesian Sparsification of Deep C-valued Networks
Ivan Nazarov, Evgeny Burnaev
Poster
Tue 12:00 On Contrastive Learning for Likelihood-free Inference
Conor Durkan, Iain Murray, George Papamakarios
Poster
Tue 13:00 Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans, Volodimir Begy, Gilles Louppe
Poster
Tue 13:00 Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost van Amersfoort, Lewis Smith, Yee-Whye Teh, Yarin Gal
Poster
Tue 14:00 Infinite attention: NNGP and NTK for deep attention networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak
Poster
Tue 14:00 Thompson Sampling via Local Uncertainty
Zhendong Wang, Mingyuan Zhou
Poster
Tue 14:00 Why bigger is not always better: on finite and infinite neural networks
Laurence Aitchison
Poster
Wed 5:00 Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff, Qinxun Bai, Fuxin Li, Wei Xu
Poster
Wed 10:00 Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
Poster
Wed 10:00 Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Poster
Wed 10:00 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Mike Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran
Poster
Wed 12:00 Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel
Poster
Wed 12:00 Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Q. Wang, Herke van Hoof
Poster
Wed 12:00 Convergence Rates of Variational Inference in Sparse Deep Learning
Badr-Eddine Chérief-Abdellatif
Poster
Wed 12:00 Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation
Konstantinos Pitas
Poster
Thu 6:00 Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Huang Hengguan, Fuzhao Xue, Hao Wang, Ye Wang
Poster
Thu 6:00 CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, Lawrence Carin
Poster
Thu 8:00 Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
Poster
Thu 8:00 Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, Jian Tang
Poster
Thu 8:00 Defense Through Diverse Directions
Christopher Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier Oliva
Poster
Thu 8:00 AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang
Poster
Thu 12:00 The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Josh V Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
Poster
Thu 14:00 Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng, Roman Bachmann, Emti Khan
Workshop
Fri 9:00 Poster Session (click to see links)