no code implementations • 11 Jan 2024 • Benjamin Peters, James J. DiCarlo, Todd Gureckis, Ralf Haefner, Leyla Isik, Joshua Tenenbaum, Talia Konkle, Thomas Naselaris, Kimberly Stachenfeld, Zenna Tavares, Doris Tsao, Ilker Yildirim, Nikolaus Kriegeskorte
The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it.
no code implementations • NeurIPS Workshop AIPLANS 2021 • Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavares
The human ability to efficiently discover causal theories of their environments from observations is a feat of nature that remains elusive in machines.
no code implementations • 6 Oct 2021 • Marlene Berke, Zhangir Azerbayev, Mario Belledonne, Zenna Tavares, Julian Jara-Ettinger
Specifically, MetaCOG is a hierarchical probabilistic model that expresses a joint distribution over the objects in a 3D scene and the outputs produced by a detector.
no code implementations • NeurIPS Workshop CAP 2020 • Zenna Tavares, Ria Das, Elizabeth Weeks, Kate Lin, Joshua B. Tenenbaum, Armando Solar-Lezama
We introduce the Causal Inductive Synthesis Corpus (CISC) -- a manually constructed collection of interactive domains.
no code implementations • ICLR 2020 • Jeevana Priya Inala, Osbert Bastani, Zenna Tavares, Armando Solar-Lezama
We show that our algorithm can be used to learn policies that inductively generalize to novel environments, whereas traditional neural network policies fail to do so.
no code implementations • 25 Mar 2019 • Zenna Tavares, Xin Zhang, Edgar Minaysan, Javier Burroni, Rajesh Ranganath, Armando Solar Lezama
The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas.
no code implementations • 16 Jan 2019 • Zenna Tavares, Javier Burroni, Edgar Minaysan, Armando Solar Lezama, Rajesh Ranganath
We develop a likelihood free inference procedure for conditioning a probabilistic model on a predicate.