Noah D. Goodman

research

∙ 05/18/2022

Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

Speakers' referential expressions often depart from communicative ideals...

Fei Fang, et al. ∙

research

∙ 03/28/2022

STaR: Bootstrapping Reasoning With Reasoning

Generating step-by-step "chain-of-thought" rationales improves language ...

Eric Zelikman, et al. ∙

research

∙ 12/05/2021

Causal Distillation for Language Models

Distillation efforts have led to language models that are more compact a...

Zhengxuan Wu, et al. ∙

research

∙ 12/01/2021

Inducing Causal Structure for Interpretable Neural Networks

In many areas, we have well-founded insights about causal structure that...

Atticus Geiger, et al. ∙

research

∙ 10/11/2021

Calibrate your listeners! Robust communication-based training for pragmatic speakers

To be good conversational partners, natural language processing (NLP) sy...

Rose E. Wang, et al. ∙

research

∙ 04/16/2021

Concadia: Tackling image accessibility with context

Images have become an integral part of online media. This has enhanced s...

Elisa Kreiss, et al. ∙

research

∙ 04/12/2021

From partners to populations: A hierarchical Bayesian account of coordination and convention

Languages are powerful solutions to coordination problems: they provide ...

Robert D. Hawkins, et al. ∙

research

∙ 05/31/2020

Learning to refer informatively by amortizing pragmatic reasoning

A hallmark of human language is the ability to effectively and efficient...

Julia White, et al. ∙

research

∙ 02/04/2020

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...

Robert D. Hawkins, et al. ∙

research

∙ 12/16/2019

Characterizing the dynamics of learning in repeated reference games

The language we use over the course of conversation changes as we establ...

Robert D. Hawkins, et al. ∙

research

∙ 11/22/2019

Continual adaptation for efficient machine communication

To communicate with new partners in new contexts, humans rapidly form ne...

Robert D. Hawkins, et al. ∙

research

∙ 09/12/2019

Analyzing machine-learned representations: A natural language case study

As modern deep networks become more complex, and get closer to human-lik...

Ishita Dasgupta, et al. ∙

research

∙ 06/04/2019

Learning to Explain: Answering Why-Questions via Rephrasing

Providing plausible responses to why questions is a challenging but crit...

Allen Nie, et al. ∙

research

∙ 05/08/2019

ShapeGlot: Learning Language for Shape Differentiation

In this work we explore how fine-grained differences between the shapes ...

Panos Achlioptas, et al. ∙

research

∙ 03/19/2019

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...

Judith Degen, et al. ∙

research

∙ 03/15/2019

Applying Probabilistic Programming to Affective Computing

Affective Computing is a rapidly growing field spurred by advancements i...

Desmond C. Ong, et al. ∙

research

∙ 11/21/2018

Joint Mapping and Calibration via Differentiable Sensor Fusion

We leverage automatic differentiation (AD) and probabilistic programming...

Jonathan P. Chen, et al. ∙

research

∙ 10/18/2018

Pyro: Deep Universal Probabilistic Programming

Pyro is a probabilistic programming language built on Python as a platfo...

Eli Bingham, et al. ∙

research

∙ 09/30/2018

An Incremental Iterated Response Model of Pragmatics

Recent Iterated Response (IR) models of pragmatics conceptualize languag...

Reuben Cohn-Gordon, et al. ∙

research

∙ 07/24/2018

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...

Robert X. D. Hawkins, et al. ∙

research

∙ 05/30/2018

Planning, Inference and Pragmatics in Sequential Language Games

We study sequential language games in which two players, each with priva...

Fereshte Khani, et al. ∙

research

∙ 02/12/2018

Evaluating Compositionality in Sentence Embeddings

An important frontier in the quest for human-like AI is compositional se...

Ishita Dasgupta, et al. ∙

research

∙ 10/12/2017

DisSent: Sentence Representation Learning from Explicit Discourse Relations

Sentence vectors represent an appealing approach to meaning: learn an em...

Allen Nie, et al. ∙

research

∙ 06/01/2017

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly...

N. Siddharth, et al. ∙

research

∙ 03/29/2017

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

We present a model of pragmatic referring expression interpretation in a...

Will Monroe, et al. ∙

research

∙ 11/22/2016

Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in ...

N. Siddharth, et al. ∙

research

∙ 10/18/2016

Deep Amortized Inference for Probabilistic Programs

Probabilistic programming languages (PPLs) are a powerful modeling tool,...

Daniel Ritchie, et al. ∙

research

∙ 08/09/2016

A pragmatic theory of generic language

Generalizations about categories are central to human understanding, and...

Michael Henry Tessler, et al. ∙

research

∙ 06/13/2016

Learning to Generate Compositional Color Descriptions

The production of color language is essential for grounded language gene...

Will Monroe, et al. ∙

research

∙ 03/19/2016

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

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) ...

Daniel Ritchie, et al. ∙

research

∙ 12/18/2015

Learning the Preferences of Ignorant, Inconsistent Agents

An important use of machine learning is to learn what people value. What...

Owain Evans, et al. ∙

research

∙ 09/09/2015

Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs

Many practical techniques for probabilistic inference require a sequence...

Andreas Stuhlmüller, et al. ∙

research

∙ 09/07/2015

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

Lightweight, source-to-source transformation approaches to implementing ...

Daniel Ritchie, et al. ∙

research

∙ 06/15/2012

A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs

We describe a dynamic programming algorithm for computing the marginal d...

Andreas Stuhlmüller, et al. ∙

research

∙ 10/25/2011

Inducing Probabilistic Programs by Bayesian Program Merging

This report outlines an approach to learning generative models from data...

Irvin Hwang, et al. ∙

Noah D. Goodman

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