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Tutorial
Mon 8:00 Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
Andrew Wilson
Poster
Tue 7:00 A Free-Energy Principle for Representation Learning
Yansong Gao · Pratik Chaudhari
Poster
Tue 7:00 Variational Bayesian Quantization
Yibo Yang · Robert Bamler · Stephan Mandt
Poster
Tue 7:00 Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama
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 Variance Reduction in Stochastic Particle-Optimization Sampling
Jianyi Zhang · Yang Zhao · Changyou Chen
Poster
Tue 9:00 Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel Brown · Russell Coleman · Ravi Srinivasan · Scott Niekum
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 9:00 Being Bayesian about Categorical Probability
Taejong Joo · Uijung Chung · Min-Gwan Seo
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 Disentangling Trainability and Generalization in Deep Neural Networks
Lechao Xiao · Jeffrey Pennington · Samuel Schoenholz
Poster
Tue 12:00 Bayesian Sparsification of Deep C-valued Networks
Ivan Nazarov · Evgeny Burnaev