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Oral
Mon May 6th 09:45 -- 10:00 AM @ Great Hall AD
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
Jacob Menick · Nal Kalchbrenner
Oral
Mon May 6th 10:00 -- 10:15 AM @ Great Hall AD
BA-Net: Dense Bundle Adjustment Networks
Chengzhou Tang · Ping Tan
Oral
Mon May 6th 10:15 -- 10:30 AM @ Great Hall AD
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Andrew Brock · Jeff Donahue · Karen Simonyan
Oral
Mon May 6th 03:15 -- 03:30 PM @ Great Hall AD
On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training
Ping Li · Phan-Minh Nguyen
Oral
Mon May 6th 03:30 -- 03:45 PM @ Great Hall AD
How Powerful are Graph Neural Networks?
Keyulu Xu · Weihua Hu · Jure Leskovec · Stefanie Jegelka
Oral
Mon May 6th 03:45 -- 04:00 PM @ Great Hall AD
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle · Michael Carbin
Oral
Tue May 7th 09:45 -- 10:00 AM @ Great Hall AD
Learning Protein Structure with a Differentiable Simulator
John Ingraham · Adam J Riesselman · Chris Sander · Debora Marks
Oral
Tue May 7th 10:00 -- 10:15 AM @ Great Hall AD
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
Curtis Hawthorne · Andriy Stasyuk · Adam Roberts · Ian Simon · Anna Huang · Sander Dieleman · Erich K Elsen · Jesse Engel · Douglas Eck
Oral
Tue May 7th 10:15 -- 10:30 AM @ Great Hall AD
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
Jack Lindsey · Samuel Ocko · Surya Ganguli · Stephane Deny
Oral
Tue May 7th 03:15 -- 03:30 PM @ Great Hall AD
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Florian Tramer · Dan Boneh
Oral
Tue May 7th 03:30 -- 03:45 PM @ Great Hall AD
Learning to Remember More with Less Memorization
Hung T Le · Truyen Tran · Svetha Venkatesh
Oral
Tue May 7th 03:45 -- 04:00 PM @ Great Hall AD
Learning Robust Representations by Projecting Superficial Statistics Out
Haohan Wang · Zexue He · Zachary Lipton · Eric P Xing
Oral
Tue May 7th 04:00 -- 04:15 PM @ Great Hall AD
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos · Patricia Rubisch · Claudio Michaelis · Matthias Bethge · Felix Wichmann · Wieland Brendel
Oral
Wed May 8th 09:45 -- 10:00 AM @ Great Hall AD
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm · Alex Fedorov · Samuel Lavoie-Marchildon · Karan Grewal · Philip Bachman · Adam Trischler · Yoshua Bengio
Oral
Wed May 8th 10:00 -- 10:15 AM @ Great Hall AD
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
James Jordon · Jinsung Yoon · Mihaela Schaar
Oral
Wed May 8th 10:15 -- 10:30 AM @ Great Hall AD
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu · Sebastian Nowozin · Ted Meeds · Richard E Turner · José Miguel Hernández Lobato · Alexander Gaunt
Oral
Wed May 8th 10:30 -- 10:45 AM @ Great Hall AD
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models
Will Grathwohl · Ricky T. Q. Chen · Jesse Bettencourt · Ilya Sutskever · David Duvenaud
Oral
Thu May 9th 09:45 -- 10:00 AM @ Great Hall AD
Pay Less Attention with Lightweight and Dynamic Convolutions
Felix Wu · Angela Fan · Alexei Baevski · Yann Dauphin · Michael Auli
Oral
Thu May 9th 10:00 -- 10:15 AM @ Great Hall AD
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Jiayuan Mao · Chuang Gan · Pushmeet Kohli · Joshua B Tenenbaum · Jiajun Wu
Oral
Thu May 9th 10:15 -- 10:30 AM @ Great Hall AD
Smoothing the Geometry of Probabilistic Box Embeddings
Xiang Li · Luke Vilnis · Dongxu Zhang · Michael Boratko · Andrew McCallum
Oral
Thu May 9th 10:30 -- 10:45 AM @ Great Hall AD
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Yikang Shen · Shawn Tan · Alessandro Sordoni · Aaron Courville
Oral
Thu May 9th 03:15 -- 03:30 PM @ Great Hall AD
Meta-Learning Update Rules for Unsupervised Representation Learning
Luke Metz · Niru Maheswaranathan · Brian Cheung · Jascha Sohl-Dickstein
Oral
Thu May 9th 03:30 -- 03:45 PM @ Great Hall AD
Temporal Difference Variational Auto-Encoder
Karol Gregor · George Papamakarios · Frederic Besse · Lars Buesing · Theophane Weber
Oral
Thu May 9th 03:45 -- 04:00 PM @ Great Hall AD
Transferring Knowledge across Learning Processes
Sebastian Flennerhag · Pablo Moreno · Neil D Lawrence · Andreas Damianou