• Publications
  • Influence
Relational inductive biases, deep learning, and graph networks
TLDR
It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Expand
Jupyter Notebooks - a publishing format for reproducible computational workflows
TLDR
Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable, is presented. Expand
Simulation as an engine of physical scene understanding
TLDR
This work proposes a model based on an “intuitive physics engine,” a cognitive mechanism similar to computer engines that simulate rich physics in video games and graphics, but that uses approximate, probabilistic simulations to make robust and fast inferences in complex natural scenes where crucial information is unobserved. Expand
psiTurk: An open-source framework for conducting replicable behavioral experiments online
TLDR
The basic architecture of the psiTurk system is described and new users are introduced to the overall goals, which aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences. Expand
Generating Plans that Predict Themselves
TLDR
A measure that quantifies the accuracy and confidence with which human observers can predict the remaining robot plan from the overall task goal and the observed initial $t$ actions in the plan is introduced. Expand
LanczosNet : Multi-Scale Deep Graph Convolutional Networks
Relational data can generally be represented as graphs. For processing such graph structured data, we propose LanczosNet, which uses the Lanczos algorithm to construct low rank approximations of theExpand
Combining Q-Learning and Search with Amortized Value Estimates
TLDR
By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning. Expand
Internal physics models guide probabilistic judgments about object dynamics
Many human activities require precise judgments about the physical properties and dynamics of multiple objects. Classic work suggests that people’s intuitive models of physics are relatively poor andExpand
Think again? The amount of mental simulation tracks uncertainty in the outcome
TLDR
This model combined a model of noisy physical simulation with a decision making strategy called the sequential probability ratio test, or SPRT, and predicted that people should use more samples when it is harder to make an accurate prediction due to higher simulation uncertainty. Expand
Relevant and Robust
TLDR
The authors discuss computational models in psychology using probabilistic models to formalize hypotheses about the beliefs of agents, their knowledge and assumptions about the world, utilizing the structured collection of probabilities regarded as priors and likelihoods. Expand
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