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Inferring the Future by Imagining the Past
Kartik Chandra · Tony Chen · Tzu-Mao Li · Jonathan Ragan-Kelley · Josh Tenenbaum

Fri Jul 28 05:15 PM -- 05:25 PM (PDT) @
Event URL: https://openreview.net/forum?id=bBZ3VsPJM9 »

A single panel of a comic book can say a lot: it shows not only where characters currently are, but also where they came from, what their motivations are, and what might happen next. More generally, humans can often infer a complex sequence of past and future events from a single snapshot image of an intelligent agent.Building on recent work in cognitive science, we offer a Monte Carlo algorithm for making such inferences. Drawing a connection to Monte Carlo path tracing in computer graphics, we borrow ideas that help us dramatically improve upon prior work in sample efficiency. This allows us to scale to a wide variety of challenging inference problems with only a handful of samples. It also suggests some degree of cognitive plausibility, and indeed we present human subject studies showing that our algorithm matches human intuitions in a variety of domains that previous methods could not scale to.

Author Information

Kartik Chandra (MIT CSAIL)
Tony Chen (Massachusetts Institute of Technology)
Tzu-Mao Li (University of California, San Diego)
Jonathan Ragan-Kelley (Massachusetts Institute of Technology)
Josh Tenenbaum (MIT)

Joshua Brett Tenenbaum is Professor of Cognitive Science and Computation at the Massachusetts Institute of Technology. He is known for contributions to mathematical psychology and Bayesian cognitive science. He previously taught at Stanford University, where he was the Wasow Visiting Fellow from October 2010 to January 2011. Tenenbaum received his undergraduate degree in physics from Yale University in 1993, and his Ph.D. from MIT in 1999. His work primarily focuses on analyzing probabilistic inference as the engine of human cognition and as a means to develop machine learning.

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