Search Results for author: Zenna Tavares

Found 7 papers, 0 papers with code

How does the primate brain combine generative and discriminative computations in vision?

no code implementations11 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.

AutumnSynth: Synthesis of Reactive Programs with Structured Latent State

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.

Program Synthesis

MetaCOG: Learning a Metacognition to Recover What Objects Are Actually There

no code implementations6 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.

Object object-detection +2

Causal Inductive Synthesis Corpus

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.

Model Discovery

Synthesizing Programmatic Policies that Inductively Generalize

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.

Imitation Learning Reinforcement Learning (RL)

The Random Conditional Distribution for Higher-Order Probabilistic Inference

no code implementations25 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.

Fairness Probabilistic Programming

Soft Constraints for Inference with Declarative Knowledge

no code implementations16 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.

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