1.6k citations · 4.4k across the 137 of their papers we have counts for
8 papers · 1 filter
A Compositional Object-Based Approach to Learning Physical Dynamics
Michael B. Chang, Tomer Ullman, Antonio Torralba +1
We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene conf…
Measuring and modeling the perception of natural and unconstrained gaze in humans and machines
Daniel Harari, Tao Gao, Nancy Kanwisher +2
Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attent…
The Emergence of Organizing Structure in Conceptual Representation
Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum
Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalize…
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue +2
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space…
Human collective intelligence as distributed Bayesian inference
Peter M. Krafft, Julia Zheng, Wei Pan +5
Collective intelligence is believed to underly the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective in…
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi +1
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient e…