MSR-IISc AI Seminar Series: GFlowNets and System 2 Deep Learning - Yoshua Bengio
GFlowNets are instances of a larger family of approaches at the intersection of generative modeling and RL that can be used to train probabilistic inference functions in a way that is related to variational inference and opens a lot of new doors, especially for brain-inspired AI. Instead of maximizing some objective (like expected return), these approaches seek to sample latent random variables from a distribution defined by an energy function, for example a posterior distribution (given past data, current observations, etc). Recent work showed how GFlowNets can be used to sample a diversity of solutions in an active learning context. We will also discuss ongoing work to explore how to train such inference machinery for learning energy-based models, to approximately marginalize over infinitely many variables, perform efficient posterior Bayesian inference and incorporate inductive biases associated with conscious processing and reasoning in humans. These inductive biases include modular knowledge representation favoring systematic generalization, the causal nature of human thoughts, concepts, explanations and plans and the sparsity of dependencies captured by reusable relational or causal knowledge. Many open questions remain to develop these ideas, which will require many collaborating minds!
Slides and video details: https://www.microsoft.com/en-us/research/video/gflownets-and-system-2-deep-learning/
MSR-IISc AI Seminar Series: https://www.microsoft.com/en-us/research/event/msriisc/talks/
Видео MSR-IISc AI Seminar Series: GFlowNets and System 2 Deep Learning - Yoshua Bengio канала Microsoft Research
Slides and video details: https://www.microsoft.com/en-us/research/video/gflownets-and-system-2-deep-learning/
MSR-IISc AI Seminar Series: https://www.microsoft.com/en-us/research/event/msriisc/talks/
Видео MSR-IISc AI Seminar Series: GFlowNets and System 2 Deep Learning - Yoshua Bengio канала Microsoft Research
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Foundation models and the next era of AIAutomating Commonsense ReasoningCausal AI for Decision MakingProject Silica Library 20223D Face Reconstruction with Dense LandmarksMicrosoft is Accelerating the Future of Aerial Autonomy[VLP Tutorial @ CVPR 2022] Image-Text Pre-training Part IMIT Technology Review’s Future Compute Conference 2022Supercharge A/B testing w/automated causal inference |Community Workshop on Microsoft's Causal ToolsUpdate on Microsoft causal open-source libraries | Community Workshop on Microsoft's Causal ToolsMSR-IISc AI Seminar Series: Where on Earth is AI Headed? - Tom M. MitchellIntroducing the Microsoft Africa Research Institute (MARI)MSR-IISc AI Seminar Series: Learning to Walk - Jitendra MalikA discussion with Sankar Das Sarma and Chetan NayakReinforcement Learning (RL) Open Source Fest 2021 | Final Presentations - Part 1Microsoft Soundscape - an Illustrated DemonstrationMicrosoft Soundscape - overview of Routes featureDeveloping a Brain-Computer Interface Based on Visual ImageryTalk: What Makes Multi-modal Learning Better than Single (Provably)A law of robustness and the importance of overparametrization in deep learning