b'Noah Goodman'

Contrastive Reinforcement Learning of Symbolic Reasoning Domains

Abstract symbolic reasoning, as required in domains such as mathematics ...
06/16/2021 ∙ by Gabriel Poesia, et al. ∙ 0 ∙ share

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Question Generation for Adaptive Education

Intelligent and adaptive online education systems aim to make high-quali...
06/08/2021 ∙ by Megha Srivastava, et al. ∙ 0 ∙ share

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Emergent Communication of Generalizations

To build agents that can collaborate effectively with others, recent res...
06/04/2021 ∙ by Jesse Mu, et al. ∙ 0 ∙ share

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Improving Compositionality of Neural Networks by Decoding Representations to Inputs

In traditional software programs, we take for granted how easy it is to ...
06/01/2021 ∙ by Mike Wu, et al. ∙ 12 ∙ share

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Language Through a Prism: A Spectral Approach for Multiscale Language Representations

Language exhibits structure at different scales, ranging from subwords t...
11/09/2020 ∙ by Alex Tamkin, et al. ∙ 0 ∙ share

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Viewmaker Networks: Learning Views for Unsupervised Representation Learning

Many recent methods for unsupervised representation learning involve tra...
10/14/2020 ∙ by Alex Tamkin, et al. ∙ 9 ∙ share

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A Simple Framework for Uncertainty in Contrastive Learning

Contrastive approaches to representation learning have recently shown gr...
10/05/2020 ∙ by Mike Wu, et al. ∙ 15 ∙ share

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Conditional Negative Sampling for Contrastive Learning of Visual Representations

Recent methods for learning unsupervised visual representations, dubbed ...
10/05/2020 ∙ by Mike Wu, et al. ∙ 7 ∙ share

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On Mutual Information in Contrastive Learning for Visual Representations

In recent years, several unsupervised, "contrastive" learning algorithms...
05/27/2020 ∙ by Mike Wu, et al. ∙ 0 ∙ share

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Investigating Transferability in Pretrained Language Models

While probing is a common technique for identifying knowledge in the rep...
04/30/2020 ∙ by Alex Tamkin, et al. ∙ 0 ∙ share

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Variational Item Response Theory: Fast, Accurate, and Expressive

Item Response Theory is a ubiquitous algorithm used around the world to ...
02/01/2020 ∙ by Mike Wu, et al. ∙ 7 ∙ share

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Multimodal Generative Models for Compositional Representation Learning

As deep neural networks become more adept at traditional tasks, many of ...
12/11/2019 ∙ by Mike Wu, et al. ∙ 29 ∙ share

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Shaping Visual Representations with Language for Few-shot Classification

Language is designed to convey useful information about the world, thus ...
11/06/2019 ∙ by Jesse Mu, et al. ∙ 0 ∙ share

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Generative Grading: Neural Approximate Parsing for Automated Student Feedback

Open access to high-quality education is limited by the difficulty of pr...
05/23/2019 ∙ by Ali Malik, et al. ∙ 0 ∙ share

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Variational Estimators for Bayesian Optimal Experimental Design

Bayesian optimal experimental design (BOED) is a principled framework fo...
03/13/2019 ∙ by Adam Foster, et al. ∙ 16 ∙ share

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Variational Bayesian Optimal Experimental Design

Bayesian optimal experimental design (BOED) is a principled framework fo...
03/13/2019 ∙ by Adam Foster, et al. ∙ 0 ∙ share

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Pragmatic inference and visual abstraction enable contextual flexibility during visual communication

Visual modes of communication are ubiquitous in modern life — from maps ...
03/11/2019 ∙ by Judith Fan, et al. ∙ 0 ∙ share

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Lost in Machine Translation: A Method to Reduce Meaning Loss

A desideratum of high-quality translation systems is that they preserve ...
02/25/2019 ∙ by Reuben Cohn-Gordon, et al. ∙ 0 ∙ share

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Tensor Variable Elimination for Plated Factor Graphs

A wide class of machine learning algorithms can be reduced to variable e...
02/08/2019 ∙ by Fritz Obermeyer, et al. ∙ 0 ∙ share

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Meta-Amortized Variational Inference and Learning

How can we learn to do probabilistic inference in a way that generalizes...
02/05/2019 ∙ by Kristy Choi, et al. ∙ 26 ∙ share

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Bias and Generalization in Deep Generative Models: An Empirical Study

In high dimensional settings, density estimation algorithms rely crucial...
11/08/2018 ∙ by Shengjia Zhao, et al. ∙ 6 ∙ share

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Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference

Stochastic optimization techniques are standard in variational inference...
10/05/2018 ∙ by Mike Wu, et al. ∙ 6 ∙ share

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Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

In modern computer science education, massive open online courses (MOOCs...
09/05/2018 ∙ by Mike Wu, et al. ∙ 0 ∙ share

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Pragmatically Informative Image Captioning with Character-Level Reference

We combine a neural image captioner with a Rational Speech Acts (RSA) mo...
04/15/2018 ∙ by Reuben Cohn-Gordon, et al. ∙ 0 ∙ share

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Multimodal Generative Models for Scalable Weakly-Supervised Learning

Multiple modalities often co-occur when describing natural phenomena. Le...
02/14/2018 ∙ by Mike Wu, et al. ∙ 0 ∙ share

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Church: a language for generative models

We introduce Church, a universal language for describing stochastic gene...
06/13/2012 ∙ by Noah Goodman, et al. ∙ 0 ∙ share

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The Infinite Latent Events Model

We present the Infinite Latent Events Model, a nonparametric hierarchica...
05/09/2012 ∙ by David Wingate, et al. ∙ 0 ∙ share

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Noah Goodman

Featured Co-authors

Stefano Ermon

Percy Liang

Joshua B. Tenenbaum

Yee Whye Teh

Dan Jurafsky

Daniel M. Roy

Tom Rainforth

Jiaming Song

Mike Wu

Vikash Mansinghka

Shengjia Zhao

Contrastive Reinforcement Learning of Symbolic Reasoning Domains

Question Generation for Adaptive Education

Emergent Communication of Generalizations

Improving Compositionality of Neural Networks by Decoding Representations to Inputs

Language Through a Prism: A Spectral Approach for Multiscale Language Representations

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

A Simple Framework for Uncertainty in Contrastive Learning

Conditional Negative Sampling for Contrastive Learning of Visual Representations

On Mutual Information in Contrastive Learning for Visual Representations

Investigating Transferability in Pretrained Language Models

Variational Item Response Theory: Fast, Accurate, and Expressive

Multimodal Generative Models for Compositional Representation Learning

Shaping Visual Representations with Language for Few-shot Classification

Generative Grading: Neural Approximate Parsing for Automated Student Feedback

Variational Estimators for Bayesian Optimal Experimental Design

Variational Bayesian Optimal Experimental Design

Pragmatic inference and visual abstraction enable contextual flexibility during visual communication

Lost in Machine Translation: A Method to Reduce Meaning Loss

Tensor Variable Elimination for Plated Factor Graphs

Meta-Amortized Variational Inference and Learning

Bias and Generalization in Deep Generative Models: An Empirical Study

Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference

Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

Pragmatically Informative Image Captioning with Character-Level Reference

Multimodal Generative Models for Scalable Weakly-Supervised Learning

Church: a language for generative models

The Infinite Latent Events Model