NIPS Proceedingsβ

Josh Tenenbaum

10 Papers

  • Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation (2016)
  • Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016)
  • Probing the Compositionality of Intuitive Functions (2016)
  • Sampling for Bayesian Program Learning (2016)
  • Deep Convolutional Inverse Graphics Network (2015)
  • Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning (2015)
  • Softstar: Heuristic-Guided Probabilistic Inference (2015)
  • Unsupervised Learning by Program Synthesis (2015)
  • Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs (2013)
  • One-shot learning by inverting a compositional causal process (2013)