Noah D. Goodman

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Uber ∙ Stanford University

Associate Professor of Psychology and Computer Science, and Linguistics (by courtesy), at Stanford University

Learning to refer informatively by amortizing pragmatic reasoning

A hallmark of human language is the ability to effectively and efficient...
05/31/2020 ∙ by Julia White, et al. ∙ 0 ∙ share

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Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks

A key property of linguistic conventions is that they hold over an entir...
02/04/2020 ∙ by Robert D. Hawkins, et al. ∙ 0 ∙ share

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Characterizing the dynamics of learning in repeated reference games

The language we use over the course of conversation changes as we establ...
12/16/2019 ∙ by Robert D. Hawkins, et al. ∙ 0 ∙ share

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Continual adaptation for efficient machine communication

To communicate with new partners in new contexts, humans rapidly form ne...
11/22/2019 ∙ by Robert D. Hawkins, et al. ∙ 0 ∙ share

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Analyzing machine-learned representations: A natural language case study

As modern deep networks become more complex, and get closer to human-lik...
09/12/2019 ∙ by Ishita Dasgupta, et al. ∙ 0 ∙ share

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Learning to Explain: Answering Why-Questions via Rephrasing

Providing plausible responses to why questions is a challenging but crit...
06/04/2019 ∙ by Allen Nie, et al. ∙ 0 ∙ share

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ShapeGlot: Learning Language for Shape Differentiation

In this work we explore how fine-grained differences between the shapes ...
05/08/2019 ∙ by Panos Achlioptas, et al. ∙ 2 ∙ share

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When redundancy is rational: A Bayesian approach to 'overinformative' referring expressions

Referring is one of the most basic and prevalent uses of language. How d...
03/19/2019 ∙ by Judith Degen, et al. ∙ 0 ∙ share

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Applying Probabilistic Programming to Affective Computing

Affective Computing is a rapidly growing field spurred by advancements i...
03/15/2019 ∙ by Desmond C. Ong, et al. ∙ 0 ∙ share

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Joint Mapping and Calibration via Differentiable Sensor Fusion

We leverage automatic differentiation (AD) and probabilistic programming...
11/21/2018 ∙ by Jonathan P. Chen, et al. ∙ 0 ∙ share

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Pyro: Deep Universal Probabilistic Programming

Pyro is a probabilistic programming language built on Python as a platfo...
10/18/2018 ∙ by Eli Bingham, et al. ∙ 0 ∙ share

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An Incremental Iterated Response Model of Pragmatics

Recent Iterated Response (IR) models of pragmatics conceptualize languag...
09/30/2018 ∙ by Reuben Cohn-Gordon, et al. ∙ 0 ∙ share

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Speakers account for asymmetries in visual perspective so listeners don't have to

Debates over adults' theory of mind use have been fueled by surprising f...
07/24/2018 ∙ by Robert X. D. Hawkins, et al. ∙ 0 ∙ share

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Planning, Inference and Pragmatics in Sequential Language Games

We study sequential language games in which two players, each with priva...
05/30/2018 ∙ by Fereshte Khani, et al. ∙ 0 ∙ share

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Evaluating Compositionality in Sentence Embeddings

An important frontier in the quest for human-like AI is compositional se...
02/12/2018 ∙ by Ishita Dasgupta, et al. ∙ 0 ∙ share

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DisSent: Sentence Representation Learning from Explicit Discourse Relations

Sentence vectors represent an appealing approach to meaning: learn an em...
10/12/2017 ∙ by Allen Nie, et al. ∙ 0 ∙ share

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Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly...
06/01/2017 ∙ by N. Siddharth, et al. ∙ 0 ∙ share

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Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding

We present a model of pragmatic referring expression interpretation in a...
03/29/2017 ∙ by Will Monroe, et al. ∙ 0 ∙ share

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Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in ...
11/22/2016 ∙ by N. Siddharth, et al. ∙ 0 ∙ share

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Deep Amortized Inference for Probabilistic Programs

Probabilistic programming languages (PPLs) are a powerful modeling tool,...
10/18/2016 ∙ by Daniel Ritchie, et al. ∙ 0 ∙ share

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A pragmatic theory of generic language

Generalizations about categories are central to human understanding, and...
08/09/2016 ∙ by Michael Henry Tessler, et al. ∙ 0 ∙ share

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Learning to Generate Compositional Color Descriptions

The production of color language is essential for grounded language gene...
06/13/2016 ∙ by Will Monroe, et al. ∙ 0 ∙ share

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Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) ...
03/19/2016 ∙ by Daniel Ritchie, et al. ∙ 0 ∙ share

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Learning the Preferences of Ignorant, Inconsistent Agents

An important use of machine learning is to learn what people value. What...
12/18/2015 ∙ by Owain Evans, et al. ∙ 0 ∙ share

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Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs

Many practical techniques for probabilistic inference require a sequence...
09/09/2015 ∙ by Andreas Stuhlmüller, et al. ∙ 0 ∙ share

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C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

Lightweight, source-to-source transformation approaches to implementing ...
09/07/2015 ∙ by Daniel Ritchie, et al. ∙ 0 ∙ share

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A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs

We describe a dynamic programming algorithm for computing the marginal d...
06/15/2012 ∙ by Andreas Stuhlmüller, et al. ∙ 0 ∙ share

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Inducing Probabilistic Programs by Bayesian Program Merging

This report outlines an approach to learning generative models from data...
10/25/2011 ∙ by Irvin Hwang, et al. ∙ 0 ∙ share

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Noah D. Goodman

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