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Tutorial
Mon 8:00 Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
Andrew Wilson
Workshop
Fri 13:45 [Session 1] P#11 Modeling Brain Microarchitecture with Deep Representation Learning
Poster
Tue 8:00 Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Lakshay Chauhan · John Alberg · Zachary Lipton
Poster
Tue 7:00 Multigrid Neural Memory
Tri Huynh · Michael Maire · Matthew Walter
Affinity Workshop
Mon 11:00 KutralNet: A Portable Deep Learning Model for Fire Recognition
Angel Ayala
Poster
Thu 9:00 Video Prediction via Example Guidance
Jingwei Xu · Harry (Huazhe) Xu · Bingbing Ni · Xiaokang Yang · Trevor Darrell
Poster
Thu 17:00 Transformer Hawkes Process
Simiao Zuo · Haoming Jiang · Zichong Li · Tuo Zhao · Hongyuan Zha
Poster
Wed 16:00 Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
Zhiyu Yao · Yunbo Wang · Mingsheng Long · Jianmin Wang
Poster
Wed 13:00 Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth · Harald Oberhauser
Poster
Thu 7:00 Time-aware Large Kernel Convolutions
Vasileios Lioutas · Yuhong Guo
Poster
Wed 8:00 Representing Unordered Data Using Complex-Weighted Multiset Automata
Justin DeBenedetto · David Chiang
Workshop
Fri 7:50 Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
Lasse F. Wolff Anthony