NeurIPS 2019 Conference Recap from Numenta

The moment we stopped understanding AI AlexNet

Чего БОИШЬСЯ то и СБУДЕТСЯ: разоблачение мифа

Избыточный риск стабильных обучающих алгоритмов. Егор Клочков

Razvan Pascanu - Continual learning for deep learning and deep reinforcement learning

Обзор статей по обучению с подкреплением с NeurIPS 2019, часть 2

NeurIPS 2019 Paper Highlight Yang Song @ Stanford University on Generative Modeling

SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen...

Advancements in Machine Learning with Sergey Levine - #355

NeurIPS 2019 Outstanding New Directions Paper Award w/ slides

Reinforcement Learning: Past, Present, and Future Perspectives | NeurIPS 2019 (w/ slides)

Efficient Computing for Deep Learning, Robotics, and AI (Vivienne Sze) | MIT Deep Learning Series

Toward a General AI-Agent Architecture | Rich Sutton, DeepMind ALberta | NeurIPS 2019

Panel Discussion: Bridging Game Theory and Deep Learning | NeurIPS 2019

NeurIPS 2019 Highlight Josh Tobin, Former OpenAI Researcher: Geometry-Aware Neural Rendering

Learning Meaningful Representations of Life - 2 | NeurIPS 2019

Learning in Dynamic Multi-Agent Environments | va Tardos | Game Theory | NeurIPS 2019

Biological and Artificial Reinforcement Learning Panel Discussion | NeurIPS 2019

MEMENTO: Further Progress Through Forgetting | NeurIPS 2019 | Reinforcement Learning

Learning Meaningful Representations of Life - 1 | NeurIPS 2019

Обзор работ по генеративным моделям с NeurIPS 2019

Обзор статей по обучению с подкреплением с NeurIPS 2019, часть 1

NeurIPS 2019: Learn to Move Walk Around 2nd place solution,

Interpretable Comparison of Distributions and Models | NeurIPS 2019

Social Intelligence | Blaise Aguera y Arcas | NeurIPS 2019