2020
- April 6, Efficient Deep Learning with Humans in the Loop, Zachary Lipton (Carnegie Mellon University)
- References: Davis Liang et al., Learning Noise-Invariant Representations for Robust Speech Recognition, Zachary C. Lipton et al., BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems, Yanyao Shen et al., Deep Active Learning for Named Entity Recognition, Aditya Siddhant et al., Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study, David Lowell et al., Practical Obstacles to Deploying Active Learning, Peiyun Hu et al., Active Learning with Partial Feedback, Shish Khetan et al., Learning From Noisy Singly-labeled Data, Yanyao Shen et al. Deep Active Learning for Named Entity Recognition, Peiyun Hu et al. Active Learning with Partial Feedback, Jonathon Byrd et al., What is the Effect of Importance Weighting in Deep Learning? Jason Yosinski et al., Understanding Neural Networks Through Deep Visualization
- March 30, Studying Generalization in Deep Learning via PAC-Bayes, Gintare Karolina Dziugaite (Element AI)
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- Few references: G.K Dziugaite, D. Roy, Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data, Huang et al., Stochastic Neural Network with Kronecker Flow, Zhou et al., Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach, Abadi et al., Deep Learning with Differential Privacy, R Herbrich, T Graepel, C Campbell, Bayes point machines, Neyshabur et al., The role of over-parametrization in generalization of neural networks, K Miyaguchi, PAC-Bayesian Transportation Bound
- A little bit of background on probably approximately correct (PAC) learning: Probably Approximately Correct Learning, A primer on PAC-Bayesian learning
- March 23, Integrating Constraints into Deep Learning Architectures with Structured Layers J. Zico Kolter (Carnegie Mellon University)
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- References: Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng, Convolutional Deep Belief Networksfor Scalable Unsupervised Learning of Hierarchical Representations. Brandon Amos, J. Zico Kolter, OptNet: Differentiable Optimization as a Layer in Neural Networks. Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter, SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud, Neural Ordinary Differential Equations. Shaojie Bai, J. Zico Kolter, Vladlen Koltun, Trellis Networks for Sequence Modeling. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Attention Is All You Need
- March 2, Rebooting AI, Gary Marcus (Robust AI)
- February 24, Is Optimization the Right Language to Understand Deep Learning? Sanjeev Arora (Princeton University)
- February 17, Adversarial Machine Learning, Ian Goodfellow (Google)
- February 10, Our Mathematical Universe, Max Tegmark (MIT)
- February 3, Nobel Lecture: Michel Mayor, Nobel Prize in Physics 2019
- January 29, How to Successfully Harness Machine Learning to Combat Fraud and Abuse, Elie Bursztein, Anti-Abuse Research Lead (Google)
2019
- December 16 & January 13, 2019-20, Variational Inference: Foundations and Innovations (Part 2, 46′), David Blei (Columbia University)
- December 2 & 9, 2019, Variational Inference: Foundations and Innovations (Part 1), David Blei (Columbia University)
- November 18, On Large Deviation Principles for Large Neural Networks, Joan Bruna (Courant Institute of Mathematical Sciences, NYU)
- November 11, 2019, Anomaly Detection using Neural Networks, Dean Langsam (BlueVine)
- October 28 & November 4, 2019, Extreme Value Theory. Paul Embrechts (ETH)
- October 7, 2019, On the Optimization Landscape of Matrix and Tensor Decomposition Problems, Tengyu Ma (Princeton University)
- September 30, 2019, Recurrent Neural Networks, Ava Soleimany (MIT)
- September 23, 2019, When deep learning does not learn, Emmanuel Abbe (EPFL and Princeton)
- July 15, 2019, Optimality in Locally Private Estimation and Learning, John Duchi (Stanford)
- July 1, 2019. Capsule Networks, Geoffrey Hinton (University of Toronto – Google Brain – Vector institute)
- June 24, 2019, A multi-perspective introduction to the EM algorithm, William M. Wells III.
- June 17, 2019, Theoretical Perspectives on Deep Learning, Nati Srebro (TTI Chicago)
- May 27, 2019. 2018 ACM Turing Award. Stanford Seminar – Human in the Loop Reinforcement Learning. Emma Brunskill (Stanford)
- May 20, 2019. How Graph Technology Is Changing Artificial Intelligence and Machine Learning. Amy E. Hodles (Neo4j), Jake Graham (Neo4j).
- May 13, 2019, 2017 Nobel Lectures in Physics. Awarded « for decisive contributions to the LIGO detector and the observation of gravitational waves ». Rainer Weiss (MIT), Barry C. Barish (Caltech) and Kip S. Thorne (Caltech)
- May 6, 2019, Accessorize to a Crime: Real and Stealthy Attacks on State-Of-The-Art Face Recognition, Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer (Carnegie Mellon University) and Michael K. Reiter (University of North Carolina Chapel Hill), paper
- April 29, 2019, Build Intelligent Fraud Prevention with ML and Graphs, Nav Mathur, Graham Ganssle
- April 15, 2019, Active Learning: Why Smart Labeling is the Future of Data Annotation, Jennifer Prendki (Figure Eight)
- April 8, 2019, Generalization, Interpolation, and Neural Nets, Alexander Rakhlin (MIT)
- April 1, 2019, Similarity learning using deep neural networks – Jacek Komorowski (Warsaw University of Technology)
- March 18/25, 2019, Deep Reinforcement Learning (First lecture of MIT course 6.S091), Lex Fridman (MIT)
- March 11, 2019, Ensembles: Boosting, Alexander Ihler University of California, Irvine)
- March 4, 2019, Dataset shift in machine learning, Peter Prettenhofer (DataRobot)
- February 25, 2019, Could Machine Learning Ever Cure Alzheimer’s Disease? – Winston Hide (Sheffield University)
- February 18, 2019, 2015 IAAA Winner Intelligent Surgical Scheduling System
- February 11, 2019, Artificial Intelligence Machine Learning Big Data, Exponential Finance – Neil Jacobstein (Singularity University)
- February 4, 2019, Bayesian Deep Learning with Edward (and a trick using Dropout) – Andrew Rowan (PrismFP)
- January 28, 2019, Ouroboros, Aggelos Kiayias (University of Edinburgh)
- January 21, 2019, Cosmos Proof of Stake – Sunny Aggrawal
- January 14, 2019, Geometric Deep Learning – Michael Bronstein (University of Lugano and Tel Aviv University)
- January 7, 2019, Deep Generative Networks as Inverse Problems – Stéphane Mallat, Ecole Normale Supérieure (ENS)
2018
