Organizing Committee
Abstract

Recent advances in machine learning have had a profound impact on computer vision. Simultaneously, success in computer vision applications has rapidly increased our understanding of some machine learning techniques, especially their applicability. This workshop will bring together researchers who are building a stronger theoretical understanding of the foundations of machine learning with computer vision researchers who are advancing our understanding of machine learning in practice.

Much of the recent growth in the use of machine learning in computer vision has been spurred by advances in deep neural networks. At the same time, new advances in other areas of machine learning, including reinforcement learning, generative models, and optimization methods, hold great promise for future impact. These raise important fundamental questions, such as understanding what influences the ability of learning algorithms to generalize, understanding what causes optimization in learning to converge to effective solutions, and understanding how to make optimization more efficient.

The workshop will include machine learning researchers who are addressing these foundational questions. It will also include computer vision researchers who are applying machine learning to a host of problems, such as visual categorization, 3D reconstruction, event and activity understanding, and semantic segmentation.

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Confirmed Speakers & Participants

  • Speaker
  • Poster Presenter
  • Attendee
  • Virtual Attendee

Workshop Schedule

Monday, February 18, 2019
TimeEventLocationMaterials
8:30 - 8:55am ESTRegistration - ICERM 121 South Main Street, Providence RI 0290311th Floor Collaborative Space 
8:55 - 9:00am ESTWelcome - ICERM Director11th Floor Lecture Hall 
9:00 - 9:45am ESTLearning spoken concepts from unlabeled audio-visual data - Karen Livescu, TTI-Chicago11th Floor Lecture Hall
10:00 - 10:30am ESTCoffee/Tea Break11th Floor Collaborative Space 
10:30 - 11:15am ESTOld-School Computer Vision Teaching Modern Deep Networks - Tal Hassner, The Open University of Israel11th Floor Lecture Hall
11:30 - 12:15pm ESTStrong-Weak Adversarial Domain Adaptation - Kate Saenko, Boston University11th Floor Lecture Hall
12:30 - 2:30pm ESTBreak for Lunch / Free Time  
2:30 - 3:15pm ESTStochastic Video Generation with a Learned Prior - Rob Fergus, NYU & Facebook11th Floor Lecture Hall
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 
3:30 - 4:15pm ESTLearning to Optimize Multigrid PDE Solvers - Ronen Basri, Weizmann Institute of Science 11th Floor Lecture Hall 
5:00 - 6:30pm ESTWelcome Reception11th Floor Collaborative Space 
Tuesday, February 19, 2019
TimeEventLocationMaterials
9:00 - 9:45am ESTAnalyzing Optimization in Deep Learning via Trajectories - Nadav Cohen, Institute for Advanced Study11th Floor Lecture Hall
10:00 - 10:30am ESTCoffee/Tea Break11th Floor Collaborative Space 
10:30 - 11:15am ESTMathematics of Deep Learning - Rene Vidal, Johns Hopkins University11th Floor Lecture Hall
11:30 - 12:15pm ESTTowards Joint Understanding of Images and Language - Svetlana Lazebnik, University of Illinois at Urbana-Champaign11th Floor Lecture Hall
12:30 - 2:30pm ESTBreak for Lunch / Free Time  
2:30 - 3:15pm ESTTask2Vec- Task Embedding for Model Recommendation - Subhransu Maji, Umass Amherst11th Floor Lecture Hall
3:30 - 5:00pm ESTPoster Session 11th Floor Collaborative Space 
Wednesday, February 20, 2019
TimeEventLocationMaterials
9:00 - 9:45am ESTLearning with Little Data - Richard Zemel, Vector Institute, Toronto11th Floor Lecture Hall 
10:00 - 10:30am ESTCoffee/Tea Break11th Floor Collaborative Space 
10:30 - 11:15am ESTMad Max - Affine Spline Insights into Deep Learning - Richard Baraniuk, Rice University11th Floor Lecture Hall
11:30 - 12:15pm ESTAccelerating atomic-scale calculations with machine learning - Andrew Peterson, School of Engineering, Brown University11th Floor Lecture Hall
12:30 - 12:40pm ESTGroup Photo11th Floor Lecture Hall 
12:40 - 2:30pm ESTBreak for Lunch / Free Time  
2:30 - 3:15pm ESTA theoretical look at adversarial examples and dataset poisoning - Tom Goldstein, University of Maryland11th Floor Lecture Hall
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45pm ESTPanel Discussion11th Floor Lecture Hall 
Thursday, February 21, 2019
TimeEventLocationMaterials
9:00 - 9:45am ESTThe K-FAC method for neural network optimization - James Martens, DeepMind11th Floor Lecture Hall
10:00 - 10:30am ESTCoffee/Tea Break11th Floor Collaborative Space 
10:30 - 11:15am ESTGradient descent aligns the layers of deep linear networks - Matus Telgarsky, University of Illinois Urbana-Champaign11th Floor Lecture Hall
11:30 - 12:15pm ESTThe Information Bottleneck theory of Deep Learning and the computational benefit of the hidden layers - Naftali Tishby, Hebrew University of Jerusalem11th Floor Lecture Hall
12:30 - 2:30pm ESTBreak for Lunch / Free Time  
2:30 - 3:15pm ESTOn The Power of Curriculum Learning in Training Deep Networks - Daphna Weinshall, Hebrew University of Jerusalem11th Floor Lecture Hall
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45pm ESTReconstructing Animals - David Jacobs, University of Maryland11th Floor Lecture Hall
Friday, February 22, 2019
TimeEventLocationMaterials
9:00 - 9:45am ESTOver-parameterized nonlinear learning - Gradient descent follows the shortest path - Mahdi Soltanolkotabi, University of Southern California11th Floor Lecture Hall
10:00 - 10:30am ESTCoffee/Tea Break11th Floor Collaborative Space 
10:30 - 11:15am ESTLearning Spatial Common Sense with Geometry-Aware Deep Networks - Katerina Fragkiadaki, Carnegie Mellon University11th Floor Lecture Hall
11:30 - 12:15pm ESTTensorial Neural Networks - Generalization of Neural Networks and Application to Model Compression - Furong Huang, University of Maryland11th Floor Lecture Hall
12:30 - 2:30pm ESTBreak for Lunch / Free Time  
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 

Associated Semester Workshops

Computer Vision
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Algebraic Vision Research Cluster
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Computational Imaging
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Introduction to the ANTs Ecosystem
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Lecture Videos

Towards Joint Understanding of Images and Language

Svetlana Lazebnik
University of Illinois, Urbana-Champaign
February 19, 2019

Stochastic Video Generation with a Learned Prior

Rob Fergus
Courant Institute of Mathematical Sciences, New York University
February 18, 2019