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