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Recent Meetings
Date  Presenter  Topic  Reading  

Random matrices 

05/03/18  Adam Charles  Matrix concentration inequalities; application: shortterm memory of linear recurrent neural networks 


04/26/18  Mikio Aoi  Random projections for least squares 


04/19/18  Farhan Damani  Lowrank matrix approximation 


04/12/18  Greg Darnell  Markov, Chebyshov, and Chernoff's inequalities; Johnson–Lindenstrauss lemma 


04/05/18  Bianca Dumitrascu  Motivation for random matrices; intro on concentration inequalities 


Information geometry 

03/15/18  Jordan Ash, Alex Beatson  The FisherRao metric and generalization of neural networks 


03/01/18  Sidu Jena  Information entropy and max entropy methods 


02/08/18  Diana Cai, Bianca Dumitrascu  Natural gradients, mirror descent and stochastic variational inference 


02/08/18  Alex Beatson, Greg Gundersen  Intro: information geometry, fdivergences, the Fisher metric, and the exponential family 


01/25/18  Sidu Jena, Archit Verma  Differential geometry overview 

Past Meetings
Date  Presenter  Topic  Reading  

Reinforcement learning & control theory 

11/16/17  Archit Verma  Robust Control 


11/09/17  Ari Seff  Optimal Control 


11/02/17  Sidu Jena, Max Wilson  Control Theory Basics 


10/26/17 
Alex Beatson  ActorCritic 


10/19/17 
Niranjani Prasad, Gregory Gundersen  QLearning 


10/12/17  Ryan Adams  Policy Gradient Methods 


Misc. previous topics 

6/22 
David Zoltowski, Mikio Aoi  Stochastic Gradient Descent as Approximate Bayesian Inference Mandt, Hoffman, Blei (2017)  
6/1 
Stephen Keeley  Understanding deep convolutional networks Mallat (2016)  
5/25 
Davit Zoltowski  Variational Inference with Normalizing Flows Rezende, Mohamed (2016)  
5/11 
Jordan Ash  Generative Adversarial Nets Ian J. Goodfellow∗ , Jean PougetAbadie† , Mehdi Mirza, Bing Xu, David WardeFarley, Sherjil Ozair‡ , Aaron Courville, Yoshua Bengio§  
5/4 
Greg Darnell  Convolutional Neural Networks Analyzed via Convolutional Sparse Coding Vardan Papyan, Yaniv Romano, Michael Elad (2016)  
4/27 
Bianca Dumitrascu 
Why does deep learning work so well? Henry W. Lin, Max Tegmark (2017) 

4/20  Yuki Shiraito  Dropout as Bayesian approximation: Representing Model Uncertainty in Deep Learning Gal & Ghahramani (2016)  
4/13  Mikio Aoi  A Probabilistic Theory of Deep Learning Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk (2015)  
3/30  Brian DePasquale  Semisupervised Learning with Deep Generative Models Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling  
3/16  Adam Charles  On the expressive power of deep learning: A tensor analysis Cohen, Sharir, Shashua (2016)  
3/9  Mikio Aoi  AutoEncoding Variational Bayes Kingma, Welling (2014)  
3/2  Bianca Dumitrascu  Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra (2014)  
2/16  Nick Roy  Understanding deep learning requires rethinking generalization Zhang, Bengio, Hardt, Recht, Vinyals (2017) 