NIPS Proceedings
^{β}
Books
Josh Tenenbaum
24 Papers
3D-Aware Scene Manipulation via Inverse Graphics
(2018)
End-to-End Differentiable Physics for Learning and Control
(2018)
Flexible neural representation for physics prediction
(2018)
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
(2018)
Learning to Exploit Stability for 3D Scene Parsing
(2018)
Learning to Infer Graphics Programs from Hand-Drawn Images
(2018)
Learning to Reconstruct Shapes from Unseen Classes
(2018)
Learning to Share and Hide Intentions using Information Regularization
(2018)
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
(2018)
Visual Object Networks: Image Generation with Disentangled 3D Representations
(2018)
Learning to See Physics via Visual De-animation
(2017)
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
(2017)
Self-Supervised Intrinsic Image Decomposition
(2017)
Shape and Material from Sound
(2017)
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)