Robin.lyMar 2010 minYoshua Bengio: From Deep Learning To ConsciousnessInterview by MIT Assistant Professor Song Han at NeurIPS 2019
Robin.lyMar 616 minIt's All About Real-world Use Cases: Rob Toews, Investor @ Highland Capital PartnersRob Toews is a venture capitalist at Highland Capital Partners focusing on investment opportunities in machine learning. He writes a regular
Robin.lyJan 287 minNuria Oliver, Co-Founder @ ELLIS: Facilitate Multi-nation Collaboration for AI Research in EuropeExclusive interview at NeurIPS 2019 about European Lab for Learning & Intelligent Systems (ELLIS)
Robin.lyJan 1614 minBuilding & Deploying AI Products At Scale - Irina Farooq, Chief Product Officer @ KineticaWhat is takes to bring machine learning out of the lab into production. How to manage a product team?
Robin.lyJan 1310 minMax Welling @ University of Amsterdam & Qualcomm: The Future of Distributed LearningEdge computing, AI research, 2020 trends, and NeurIPS 2019 paper highlights
Robin.lyJan 811 minPeter Richtarik @ KAUST, Saudi Arabia: Managing Data Privacy In The AI EraImportance and limitations of data privacy, and the past and future of machine learning.
Robin.lyJan 76 minLiwei Wang @ Peking University: How to Apply Machine Learning on Data PrivacyDifferential Privacy, Machine Learning, Data Privacy, NeurIPS 2019, Peking University, Difference between China and U.S., 2020 trends
Robin.lyJan 66 minCharles Onu @Mila & Mcgill University: Applying Machine Learning in Healthcare for Social InnovationCharles Onu is a PhD student and ML Researcher at Mila, as well as the co-founder of Ubenwa, a social enterprise use AI to save newborn live
Robin.lyJan 55 minTengyu Ma @Stanford - Understand Deep Learning: Improving Efficiency, Reliability & InterpretabilityDr. Ma introduced NeurIPS 2019 paper highlights, the work behind the Honorable Mention ACM Award, insights on the future of AI research
Robin.lyJan 42 min[NeurIPS 2019 Highlight] Harm Ven Seijen @ Microsoft : Logarithmic Mapping in Reinforcement LearningThis episode is an interview with Harm van Seijan from Microsoft Research Montreal, discussing highlights from his paper, "Using a Logarithm
Robin.lyJan 42 min[NeurIPS 2019 Highlight] Sebastian Goldt @ CNRS, Paris: Stochastic Gradient Descent for two-layer NNThis episode is an interview with Sebastian Institute de Physique Theorique, discussing highlights from his paper "Dynamics of stochastic gr
Robin.lyJan 41 min[NeurIPS 2019 Highlight] Jingwei Liang @ Cambridge : Multipliers and Adaptive AccelerationThis episode is an interview with Jingwei Liang from University of Cambridge, discussing highlights from his paper, "Trajectory of Alternati
Robin.lyJan 32 min[NeurIPS 2019 Highlight] Yang Song @ Stanford: Using Score Functions for Generative ModelingYang discusses key contributions from his spotlight paper "Generative Modeling by Estimating Gradients of the Data Distribution".
Robin.lyJan 31 min[NeurIPS 2019 Highlight] Daniel Levy @ Stanford: Sufficient Geometries for Gradient MethodsThis episode is an interview with Daniel Levy from Stanford University, discussing highlights from his paper, "Necessary and Sufficient Geom
Robin.lyDec 19, 20195 min [NeurIPS 2019 Highlight] Josh Tobin, Former OpenAI Researcher: Geometry-aware Neural RenderingThis episode is an interview with Josh Tobin, a former OpenAI Researcher, discussing highlights from his paper, Geometry-aware Neural Rendering, which was ac...
Robin.lyDec 19, 20192 min[NeurIPS 2019 Highlight] Vincent Sitzmann @ Stanford: Scene Representation NetworksThis episode is an interview with Vincent Sitzmann from Stanford University, discussing highlights from his paper, "Scene Representation Networks: Continuous...
Robin.lyDec 19, 20192 min[NeurIPS 2019 Honorable Mention Outstanding Paper] Jubran & Maalouf on Least-Mean-Squares SolversThis episode is an interview with Honorable Mention Outstanding Paper Award winners Ibrahim Jubran and Alaa Maalouf from Haifa University, discussing highlig...
Robin.lyDec 18, 20192 min[NeurIPS 2019 Highlight] Meena Jagadeesan @ Harvard: Understanding Sparse JL for Feature HashingThis episode is an interview with Meena from Harvard University, discussing highlights from her paper, "Understanding Sparse JL for Feature Hashing," accepte...
Robin.lyDec 18, 20192 min[NeurIPS 2019 Highlight] Yair Carmon @ Stanford: Unlabeled Data Improves Adversarial RobustnessThis episode is an interview with Yair Carmon from Stanford University, discussing highlights from his paper, "Unlabeled Data Improves Adversarial Robustness...
Robin.lyDec 18, 20199 min[NeurIPS 2019 Outstanding New Directions Paper Award] Nagarajan & Kolter on Uniform ConvergenceNegative results showing that many existing bounds on the performance of deep learning algorithms don’t do what they claim
