IPAM sponsored the New Deep Learning Techniques workshop last year (February 5 - 9, 2018). thank you to the organizers (Xavier Bresson, Michael Bronstein, Joan Bruna, Yann LeCun, Stanley Osher, Arthur Szlam for making these videos available. )
In recent years, artificial neural networks a.k.a. deep learning have significantly improved the fields of computer vision, speech recognition, and natural language processing. The success relies on the availability of large-scale datasets, the developments of affordable high computational power, and basic deep learning operations that are sound and fast as they assume that data lie on Euclidean grids. However, not all data live on regular lattices. 3D shapes in computer graphics represent Riemannian manifolds. In neuroscience, brain activity (fMRI) is encoded on the structural connectivity network (sMRI). In genomics, the human body functionality is expressed through DNA, RNA, and proteins that form the gene regulatory network (GRN). In social sciences, people interact through networks. Eventually, data in communication networks are structured by graphs like the Internet or road traffic networks.
Deep learning that has originally been developed for computer vision cannot be directly applied to these highly irregular domains, and new classes of deep learning techniques must be designed. This is highly challenging as most standard data analysis tools cannot be used on heterogonous data domains. The workshop will bring together experts in mathematics (statistics, harmonic analysis, optimization, graph theory, sparsity, topology), machine learning (deep learning, supervised & unsupervised learning, metric learning) and specific applicative domains (neuroscience, genetics, social science, computer vision) to establish the current state of these emerging techniques and discuss the next directions.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
Here are the videos (and some slides):
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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