On January 18-21, 2019 the Center of Mathematical Sciences and Applications will be hosting a workshop on the Geometric Analysis Approach to AI.
This workshop will focus on the theoretic foundations of AI, especially various methods in Deep Learning. The topics will cover the relationship between deep learning and optimal transportation theory, DL and information geometry, DL Learning and information bottle neck and renormalization theory, DL and manifold embedding and so on. Furthermore, the recent advancements, novel methods, and real world applications of Deep Learning will also be reported and discussed.
The workshop will take place from January 18th to January 23rd, 2019. In the first four days, from January 18th to January 21, the speakers will give short courses; On the 22nd and 23rd, the speakers will give conference representations. This workshop is organized by Xianfeng Gu and Shing-Tung Yau.
The workshop will be held in room G10 of the CMSA, located at 20 Garden Street, Cambridge, MA.
For a list of lodging options convenient to the Center, please visit our recommended lodgings page.
|Friday, Jan. 18|
|9:00-10:00||Eric Xing||Carnegie Mellon University|
|10:00-11:00am||Jiajun Wu||Massachusetts Institute
|Physical Scene Understanding with Compositional Structure|
|11:30-12:30pm||Tomaso Poggio||Massachusetts Institute of Technology||Three puzzles in the theory of Deep Learning|
|2:00-3:00pm||Vivienne Sze||Massachusetts Institute of Technology||Enabling Efficient Processing of Deep Neural Networks|
|Stony Brook University||GAN, Optimal Transport and Monge-Ampere Equation|
|Saturday, Jan. 19|
|9:00-10:00am||Johannes Schmidt-Hieber||University of Twente||From network sparsity to statistical guarantees|
|10:00-11:00am||Steven Skiena||Stony Brook University||Word and Graph Embeddings, with Applications|
|11:30-12:30pm||Guy Bresler||Massachusetts Institute
|Reducibility and Computational Lower Bounds for some High-Dimensional Statistics Problems|
|2:00-3:00pm||Yingnian Wu||UCLA||A representational theory of grid cells|
|3:00-4:00pm||Francesco Orabona||Boston University||Parameter-free Machine Learning through Coin Betting|
|Northeastern University||Graph Distance from the Topological View of Non-backtracking Cycles|
Sunday, Jan. 20
No talks Sunday due to inclement weather.
|Monday, Jan. 21|
|9:00-10:00am||Cengiz Pehlevan||Harvard||A similarity-based normative theory of biologically-plausible learning in neural networks
Slides available upon request
|10:00-11:00am||Brian Kulis||Boston University||Small-Variance Asymptotics for Large-Scale Learning|
|11:30-12:30pm||Minh Hoai Nguyen||Stony Brook University||Slides|
|DiDi Research America||Reinforcement Learning with Applications in Ridesharing and Transportation|
|3:00-4:00pm||Gangqiang Xia||Morgan Stanley||Alternative Data and Machine Learning in Fixed Income Modeling|
|4:30-5:30pm||Zhu, Juhua||Argus Investment
|The Application of Machine Learning in Quantitative Trading|
|Tuesday, Jan. 22|
|9:00-10:00am||Kate Saenko||Boston University||Adaptive Deep Learning for Visual Understanding|
|10:00-11:00am||Na Lei||Dalian University of Technology||Tropical Geometry, OMT and Neural Network|
|11:30-12:30pm||Sarah Adel Bargal||Boston University||Grounding Deep Models of Visual Data|
|2:00-3:00pm||Alan Yuille||Johns Hopkins University|
|3:00-4:00pm||Donghui Yan||UMass Dartmouth||Random projection forests|
|4:30-5:30pm||Yun Raymond Fu||Northeastern University|