b'Noah Goodman'

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it

Noah Goodman

Featured Co-authors

Stefano Ermon

Percy Liang

Yee Whye Teh

Joshua B. Tenenbaum

Dan Jurafsky

Daniel M. Roy

Jiaming Song

Tom Rainforth

Mike Wu

Vikash Mansinghka

Shengjia Zhao

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