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Attention is All you Need - NIPS

papers.nips.cc

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a …

  Information, System, 2017, Processing, Inps, Neural, Neural information processing systems, Nips 2017

A Unified Approach to Interpreting Model Predictions - NIPS

papers.nips.cc

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. 2. We then show that game theory results guaranteeing a unique solution apply to the entire class of additive feature attribution methods (Section 3) and propose SHAP values as …

  Information, System, 2017, Processing, Inps, Neural, Neural information processing systems, Nips 2017

Neural Discrete Representation Learning

arxiv.org

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. arXiv:1711.00937v2 [cs.LG] 30 May 2018 “posterior collapse” issue which has been problematic with many VAE models that have a powerful decoder, often caused by latents being ignored. Additionally, it is the first discrete latent VAE model

  Information, System, 2017, Processing, Representation, Inps, Neural, Neural information processing systems, Nips 2017

2017 NIPS Poster for web

media.nips.cc

LONG BEACH CA | DEC 4 - 9 | NIPS.CC NIPS 2017 TUTORIALS - DEC 4TH Statistical Relational Artificial Intelligence: Logic, Probability and Computation Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan Reinforcement Learning with People Emma Brunskill A Primer on Optimal Transport Marco Cuturi, Justin Solomon

  2017, Inps, 2017 nips, Nips 2017

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