Tutorial Selections for NeurIPS 2020!

  • Abstraction & Reasoning in AI systems: Modern Perspectives
    (Francois Chollet (Google), Melanie Mitchell (Santa Fe Institute), Christian Szegedy (Google))
  • Advances in Approximate Inference
    (Yingzhen Li, Cheng Zhang (Microsoft Research))
  • Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems
    (Praveen Chandar (Spotify), Fernando Diaz (Microsoft Research), Brian St. Thomas (Spotify))
  • Deep Conversational AI
    (Pascale Fung, Zhaojiang Lin andAndrea Madotto (Hong Kong University of Science and Technology))
  • Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization
    (David Duvenaud (University of Toronto), Matt Johnson (Google), Zico Kolter (Carnegie Melon University))
  • Designing Learning Dynamics
    (Wojtek Czarnecki, Marta Garnelo and David Balduzzi (DeepMind))
  • Equivariance and Covariance in Deep Neural Networks
    (Taco Cohen (University of Amsterdam) and Risi Kondor (University of Chicago))
  • Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities
    (Hima Lakkaraju (Harvard), Julius Adebayo (MIT), Sameer Singh (UC Irvine))
  • Federated Learning and Analytics: Industry Meets Academia(Peter Kairouz (Google), Brendan McMahan (Google), and Virginia Smith (CMU))
  • Machine Learning for Astrophysics and Astrophysics Problems for Machine Learning
    (David W Hogg, Kate Storey-Fisher (New York University))
  • Offline Reinforcement Learning From Algorithm Design to Practical Applications
    (Sergey Levine and Aviral Kumar (University of Berkley))
  • Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning
    (Dustin Tran, Jasper Snoek, Balaji Lakshminarayanan (Google Brain))
  • RL and Optimization
    (Sham Kakade (University of Washington), Martha White (University of Alberta), Nicolas Le Roux (Google))
  • Sketching and Streaming Algorithms(Jelani Nelson (University of California, Berkeley))
  • The Beautiful Intertwining of Causal Inference, Experimental Design and Reinforcement Learning
    (Susan Murphy (Harvard University))
  • There and Back Again: A Tale of Slopes and Expectations
    (Marc Deisenroth (University College London), Cheng Soon Ong (Data 61, Australian National University))
  • Where Neuroscience meets AI (And What’s in Store for the Future)
    (Jane Wang, Adam Marblestone, Kevin Miller (DeepMind))




Tweets sent to this account are not actively monitored. To contact us please go to http://neurips.cc/Help/Contact

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

We Need To Unlock The Power Of Cause And Effect

Intro to Quantum Computing- Pawel Gora

5 Next Gen Accelerators for Transforming Value in Source to Pay

How China and the USA are changing with the coming of AI?

China and USA- Change with AI

AIRElumni — Patricia Cabral

How SRI’s AI technology is changing the way healthcare is delivered

WooCommerce Chatbots & Live Chat



Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Neural Information Processing Systems Conference

Neural Information Processing Systems Conference

Tweets sent to this account are not actively monitored. To contact us please go to http://neurips.cc/Help/Contact

More from Medium

Courtroom superheroes. Record week for ECC’s exclusive firm of lawyers, M1 Legal

Fake News: Bakemasking

Retro I Do What I Want Cat Funny Cat Lover T Shirt