NIPS Proceedingsβ

Jascha Sohl-Dickstein

8 Papers

  • Invertible Convolutional Flow (2019)
  • Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent (2019)
  • Adversarial Examples that Fool both Computer Vision and Time-Limited Humans (2018)
  • PCA of high dimensional random walks with comparison to neural network training (2018)
  • REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models (2017)
  • SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability (2017)
  • Exponential expressivity in deep neural networks through transient chaos (2016)
  • Deep Knowledge Tracing (2015)