Saturday Morning Videos: IPAM Workshop on New Deep Learning Techniques

Yann mentioned it on its twitter feed, the videos and slides of the IPAM workshop on New Deep Learning Techniques is out. Enjoy !

Samuel Bowman (New York University)
Toward natural language semantics in learned representations






Emily Fox (University of Washington)
Interpretable and Sparse Neural Network Time Series Models for Granger Causality Discovery






Ellie Pavlick (University of Pennsylvania)
Should we care about linguistics?






Leonidas Guibas (Stanford University)
Knowledge Transport Over Visual Data




Yann LeCun (New York University)
Public Lecture: Deep Learning and the Future of Artificial Intelligence






Alán Aspuru-Guzik (Harvard University)
Generative models for the inverse design of molecules and materials






Daniel Rueckert (Imperial College)
Deep learning in medical imaging: Techniques for image reconstruction, super-resolution and segmentation







Kyle Cranmer (New York University)
Deep Learning in the Physical Sciences







Stéphane Mallat (École Normale Supérieure)
Deep Generative Networks as Inverse Problems






Michael Elad (Technion - Israel Institute of Technology)
Sparse Modeling in Image Processing and Deep Learning







Yann LeCun (New York University)
Public Lecture: AI Breakthroughs & Obstacles to Progress, Mathematical and Otherwise






Xavier Bresson (Nanyang Technological University, Singapore)
Convolutional Neural Networks on Graphs







Federico Monti (Universita della Svizzera Italiana)
Deep Geometric Matrix Completion: a Geometric Deep Learning approach to Recommender Systems






Joan Bruna (New York University)
On Computational Hardness with Graph Neural Networks








Jure Leskovec (Stanford University)
Large-scale Graph Representation Learning







Arthur Szlam (Facebook)
Composable planning with attributes






Yann LeCun (New York University)
A Few (More) Approaches to Unsupervised Learning







Sanja Fidler (University of Toronto)
Teaching Machines with Humans in the Loop



Raquel Urtasun (University of Toronto)
Deep Learning for Self-Driving Cars




Pratik Chaudhari (University of California, Los Angeles (UCLA))
Unraveling the mysteries of stochastic gradient descent on deep networks







Stefano Soatto (University of California, Los Angeles (UCLA))
Emergence Theory of Deep Learning






Tom Goldstein (University of Maryland)
What do neural net loss functions look like?







Stanley Osher (University of California, Los Angeles (UCLA))
New Techniques in Optimization and Their Applications to Deep Learning and Related Inverse Problems







Michael Bronstein (USI Lugano, Switzerland)
Deep functional maps: intrinsic structured prediction for dense shape correspondence






Sainbayar Sukhbaatar (New York University)
Deep Architecture for Sets and Its Application to Multi-agent Communication






Zuowei Shen (National University of Singapore)
Deep Learning: Approximation of functions by composition







Wei Zhu (Duke University)
LDMnet: low dimensional manifold regularized neural networks












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