2017 NIPS Poster for web

NIPS 2017 . LONG BEACH CA | DEC 4 - 9 | TUTORIALS - DEC 4TH INVITED SPEAKERS - DEC 5TH - 7TH SYMPOSIA - DEC 7TH. Statistical Relational Artificial Intelligence: Logic, Pieter Abbeel (UC Berkely, Open AI) Interpretable Machine learning Probability and Computation deep learning for Robotics Andrew G. Wilson Jason Yosinski Patrice Simard Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan Rich Caruana William Herlands Kate Crawford (Microsoft Research). reinforcement learning with People The Trouble with Bias deep reinforcement learning Emma Brunskill Pieter Abbeel Yan Duan David Silver Brendan J Frey ( deep Genomics, Vector Institute, U. Toronto) Satinder Singh Junhyuk Oh Rein Houthooft A Primer on Optimal Transport Why AI Will Make it Possible to Reprogram the Human Genome Marco Cuturi, Justin Solomon Kinds of Intelligence: Types, Tests and Geometric deep learning on Graphs & Manifolds Lise Getoor (UC Santa Cruz) Meeting the Needs of Society Michael Bronstein, Joan Bruna, Arthur Szlam, Xavier Bresson, The Unreasonable Effectiveness of Structure Jos Hern ndez-Orallo Zoubin Ghahrama

Learning State Representations John Platt (Google) Energy Strategies to Decrease CO2 Emissions Yee Whye Teh (Oxford, DeepMind) On Bayesian Deep Learning and Deep Bayesian Learning SYMPOSIA - DEC 7TH Interpretable Machine Learning Andrew G. Wilson · Jason Yosinski · Patrice Simard Rich Caruana · William Herlands Deep Reinforcement Learning

Tags:

  2017, Learning, Energy, Deep, Reinforcement, Inps, Deep learning, Deep reinforcement learning, 2017 nips

Information

Related search queries