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Poster
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Thu 7:45
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Learning Deep Disentangled Embeddings With the F-Statistic Loss
Karl Ridgeway · Michael Mozer
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Workshop
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Sat 12:10
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TrackML, a Particle Physics Tracking Machine Learning Challenge, Jean-Roch Vlimant (Caltech), Vincenzo Innocente, Andreas Salzburger (CERN), Isabelle Guyon (ChaLearn), Sabrina Amrouche, Tobias Golling, Moritz Kiehn (Geneva University),David Rousseau∗, Yet
Andrey Ustyuzhanin · jean-roch vlimant
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Poster
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Thu 7:45
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Toddler-Inspired Visual Object Learning
Sven Bambach · David Crandall · Linda Smith · Chen Yu
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Spotlight
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Wed 6:55
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End-to-End Differentiable Physics for Learning and Control
Filipe de Avila Belbute Peres · Kevin Smith · Kelsey Allen · Josh Tenenbaum · J. Zico Kolter
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Poster
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Wed 7:45
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Critical initialisation for deep signal propagation in noisy rectifier neural networks
Arnu Pretorius · Elan van Biljon · Steve Kroon · Herman Kamper
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Tutorial
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Mon 11:30
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Statistical Learning Theory: a Hitchhiker's Guide
John Shawe-Taylor · Omar Rivasplata
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Workshop
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Sat 12:30
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DeepPavlov: An Open Source Library for Conversational AI
Yury Kuratov
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Spotlight
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Wed 7:20
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Minimax Statistical Learning with Wasserstein distances
Jaeho Lee · Maxim Raginsky
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Workshop
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Fri 5:00
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Modeling the Physical World: Learning, Perception, and Control
Jiajun Wu · Kelsey Allen · Kevin Smith · Jessica Hamrick · Emmanuel Dupoux · Marc Toussaint · Josh Tenenbaum
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Poster
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Wed 7:45
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Minimax Statistical Learning with Wasserstein distances
Jaeho Lee · Maxim Raginsky
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Poster
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Wed 7:45
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Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
Alyson Fletcher · Parthe Pandit · Sundeep Rangan · Subrata Sarkar · Philip Schniter
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Poster
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Wed 7:45
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Computationally and statistically efficient learning of causal Bayes nets using path queries
Kevin Bello · Jean Honorio
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