high energy physics (hep) - artificial intelligence (ai)

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Journal Club


Upcoming talks

Date Speaker Topic Paper(s) Slides

Past talks

Date Speaker Topic Paper(s) Slides
4/30/2019 Dalit Engelhardt Controlling stochastic evolution with deep reinforcement learning arxiv:1903.11373 Yes
4/16/2019   Cancelled    
4/2/2019   Cancelled    
3/19/2019 Josh Batson Noise2Self: Blind Denoising by Self-Supervision arxiv:1901.11365 Yes
3/5/2019 Boris Hanin Complexity of Linear Regions in Deep Networks arxiv:1901.09021 Yes
2/19/2019   Cancelled    
2/5/2019 Jaehoon Lee Understanding wide neural networks arxiv:1711.00165 Yes
1/22/2019 James Giammona Neural Ordinary Differential Equations arxiv:1806.07366 Yes
1/8/2019 Yoni Kahn A Statistical Field Theory analogy arxiv:1710.06570 Yes
12/25/2018   No Talk, Winter Break    
12/11/2018 Marat Freytsis Weak supervision, noisy labels, and error propagation arxiv:1706.09451, arxiv:1708.02949, arxiv:1402.5902 Yes
11/27/2018 Stefano Spigler & Mario Geiger A jamming transition from under- to over-parametrization affects loss landscape and generalization arxiv:1809.09349, arxiv:1810.09665 Yes
11/13/2018 Riccardo Zecchina The evolving entropy landscape of deep network Many  
10/30/2018 Marylou Gabrié Entropy and mutual information in models of deep neural networks arxiv:1805.09785 Yes
10/16/2018 Sho Yaida Fluctuation-dissipation relations for SGD arxiv:1810.00004 Yes
10/2/2018 Noam Brown Beating Humans at Poker science.aao1733  
9/18/2018 Josh Batson Learning to Denoise Without Clean Data arXiv:1803.04189 Yes
9/4/2018 Levent Sagun Continuation of the Exploration of the Loss Landscape: an Empirical View   Yes
8/21/2018 Boris Hanin Everything You Wanted to Know About the Loss Surface but Were Afraid to Ask – The Talk Many Yes
8/7/2018 Yasaman Bahri Deep Learning and Quantum Entanglement arXiv:1704.01552 Yes
7/24/2018 Adam Brown Fun in High-Dimensional Spaces    
7/10/2018   Cancelled due to ICML    
6/26/2018 David Schwab Renormalizing Data Koch-Janusz & Ringel and past work and a future idea Not Yet
6/19/2018 Everyone Generative Query Networks – Discussion Eslami, et al. No
6/12/2018 Ethan Dyer InfoGAN - Disentangled Latent Representations arXiv:1606.03657 Yes
5/29/2018 Dima Krotov Dense Associative Memory & Adversarial Inputs NIPS 2016, arxiv:1701.00939 Yes
5/15/2018 Eric Mintun Attention e.g. arxiv:1409.0473, arxiv:1702.00887, arxiv:1706.03762 Yes
5/1/2018 Adam Brown A Critical Review of Quantum Machine Learning nature23474, arxiv:1611.09347 Yes
4/17/2018 Josh Batson Equivariance in Deep Learning arxiv:1703.06114, arxiv:1801.10130 Yes
4/3/2018 Shay Moran Generalization and Simplification in Statistical Learning   Yes
3/20/2018 Yoni Kahn Training Pruned Neural Networks arxiv:1803.03635 No
3/6/2018 Paul Christiano AI Safety, Human Feedback, Robustness + Transparency arxiv:1706.03741 + others Yes
2/20/2018 Jamie Sully Adversarial Examples arxiv:1801.02774 Yes
2/6/2018 Boris Hanin When do exploding and vanishing gradients happen? arxiv:1801.03744 Yes
1/23/2018 Guy Gur-Ari Three Factors Influencing Minima in SGD arxiv:1711.04623 Yes
1/9/2018 Max Kleiman-Weiner Hierarchical RL and Eigenoptions arxiv:1703.00956, arxiv:1710.11089 Yes
12/26/2017 Yoni Kahn Spontaneous Symmetry Breaking and Informtion Bottlenecks arxiv:1710.06096 Yes
12/12/2017 Eric Mintun Capsule Networks Hinton, Krizhevsky, Wang, arxiv:1710.09829 Yes
11/21/2017 Jared Kaplan Varitional Information Maximizing Exploration and Curiosity-Driven Self-Supervision arxiv:1605.09674, arxiv:1705.05363 Yes
11/7/2017 DJ Strouse Information Bottlenecks and Variational Inference arxiv:1612.00410 No
10/24/2017 Jamie Sully AlphaGo Zero nature24270 Yes
10/10/2017 Dan Roberts Intro to RL and Distributional RL arxiv:1707.06887 Yes
9/26/2017 Ethan Dyer Learning to RL & RL2 arxiv:1611.02779, arxiv:1611.05763 Yes
9/12/2017 Guy Gur-Ari Learning to Learn and Metalearning arxiv:1606.04474, arxiv:1703.03400 Yes
8/29/2017 Jaehoon Lee KFAC arxiv:1503.05671 No