Christopher Ré

Rethinking Neural Operations for Diverse Tasks

An important goal of neural architecture search (NAS) is to automate-awa...
03/29/2021 ∙ by Nicholas Roberts, et al. ∙ 7 ∙ share

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Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation

Labeling data for modern machine learning is expensive and time-consumin...
03/03/2021 ∙ by Mayee F. Chen, et al. ∙ 12 ∙ share

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Robustness Gym: Unifying the NLP Evaluation Landscape

Despite impressive performance on standard benchmarks, deep neural netwo...
01/13/2021 ∙ by Karan Goel, et al. ∙ 2 ∙ share

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Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

Modern neural network architectures use structured linear transformation...
12/29/2020 ∙ by Tri Dao, et al. ∙ 6 ∙ share

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No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems

In real-world classification tasks, each class often comprises multiple ...
11/25/2020 ∙ by Nimit S. Sohoni, et al. ∙ 6 ∙ share

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Sharp Bias-variance Tradeoffs of Hard Parameter Sharing in High-dimensional Linear Regression

Hard parameter sharing for multi-task learning is widely used in empiric...
10/22/2020 ∙ by Hongyang R. Zhang, et al. ∙ 10 ∙ share

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Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation

A challenge for named entity disambiguation (NED), the task of mapping t...
10/20/2020 ∙ by Laurel Orr, et al. ∙ 10 ∙ share

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From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

Similarity-based Hierarchical Clustering (HC) is a classical unsupervise...
10/01/2020 ∙ by Ines Chami, et al. ∙ 4 ∙ share

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Leveraging Organizational Resources to Adapt Models to New Data Modalities

As applications in large organizations evolve, the machine learning (ML)...
08/23/2020 ∙ by Sahaana Suri, et al. ∙ 12 ∙ share

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HiPPO: Recurrent Memory with Optimal Polynomial Projections

A central problem in learning from sequential data is representing cumul...
08/17/2020 ∙ by Albert Gu, et al. ∙ 24 ∙ share

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Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

Classifiers in machine learning are often brittle when deployed. Particu...
08/15/2020 ∙ by Karan Goel, et al. ∙ 18 ∙ share

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GRIP: A Graph Neural Network Accelerator Architecture

We present GRIP, a graph neural network accelerator architecture designe...
07/27/2020 ∙ by Kevin Kiningham, et al. ∙ 0 ∙ share

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Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings

Our goal is to enable machine learning systems to be trained interactive...
06/26/2020 ∙ by Mayee F. Chen, et al. ∙ 1 ∙ share

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Contextual Embeddings: When Are They Worth It?

We study the settings for which deep contextual embeddings (e.g., BERT) ...
05/18/2020 ∙ by Simran Arora, et al. ∙ 0 ∙ share

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Machine Learning on Graphs: A Model and Comprehensive Taxonomy

There has been a surge of recent interest in learning representations fo...
05/07/2020 ∙ by Ines Chami, et al. ∙ 44 ∙ share

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Understanding and Improving Information Transfer in Multi-Task Learning

We investigate multi-task learning approaches that use a shared feature ...
05/02/2020 ∙ by Sen Wu, et al. ∙ 0 ∙ share

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On the Generalization Effects of Linear Transformations in Data Augmentation

Data augmentation is a powerful technique to improve performance in appl...
05/02/2020 ∙ by Sen Wu, et al. ∙ 5 ∙ share

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Low-Dimensional Hyperbolic Knowledge Graph Embeddings

Knowledge graph (KG) embeddings learn low-dimensional representations of...
05/01/2020 ∙ by Ines Chami, et al. ∙ 10 ∙ share

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Ivy: Instrumental Variable Synthesis for Causal Inference

A popular way to estimate the causal effect of a variable x on y from ob...
04/11/2020 ∙ by Zhaobin Kuang, et al. ∙ 2 ∙ share

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Assessing Robustness to Noise: Low-Cost Head CT Triage

Automated medical image classification with convolutional neural network...
03/17/2020 ∙ by Sarah M. Hooper, et al. ∙ 3 ∙ share

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Understanding the Downstream Instability of Word Embeddings

Many industrial machine learning (ML) systems require frequent retrainin...
02/29/2020 ∙ by Megan Leszczynski, et al. ∙ 0 ∙ share

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Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

Weak supervision is a popular method for building machine learning model...
02/27/2020 ∙ by Daniel Y. Fu, et al. ∙ 6 ∙ share

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Hyperbolic Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNs) embed nodes in a graph into E...
10/28/2019 ∙ by Ines Chami, et al. ∙ 23 ∙ share

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Multi-Resolution Weak Supervision for Sequential Data

Since manually labeling training data is slow and expensive, recent indu...
10/21/2019 ∙ by Frederic Sala, et al. ∙ 16 ∙ share

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PipeMare: Asynchronous Pipeline Parallel DNN Training

Recently there has been a flurry of interest around using pipeline paral...
10/09/2019 ∙ by Bowen Yang, et al. ∙ 43 ∙ share

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Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels

Many real-world video analysis applications require the ability to ident...
10/07/2019 ∙ by Daniel Y. Fu, et al. ∙ 37 ∙ share

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Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging

Machine learning models for medical image analysis often suffer from poo...
09/27/2019 ∙ by Luke Oakden-Rayner, et al. ∙ 24 ∙ share

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Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

In real-world machine learning applications, data subsets correspond to ...
09/13/2019 ∙ by Vincent S. Chen, et al. ∙ 34 ∙ share

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Overton: A Data System for Monitoring and Improving Machine-Learned Products

We describe a system called Overton, whose main design goal is to suppor...
09/07/2019 ∙ by Christopher Ré, et al. ∙ 0 ∙ share

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On the Downstream Performance of Compressed Word Embeddings

Compressing word embeddings is important for deploying NLP models in mem...
09/03/2019 ∙ by Avner May, et al. ∙ 5 ∙ share

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Sparse Recovery for Orthogonal Polynomial Transforms

In this paper we consider the following sparse recovery problem. We have...
07/19/2019 ∙ by Anna Gilbert, et al. ∙ 0 ∙ share

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Scene Graph Prediction with Limited Labels

Visual knowledge bases such as Visual Genome power numerous applications...
04/25/2019 ∙ by Vincent S. Chen, et al. ∙ 6 ∙ share

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Low-Memory Neural Network Training: A Technical Report

Memory is increasingly often the bottleneck when training neural network...
04/24/2019 ∙ by Nimit Sharad Sohoni, et al. ∙ 0 ∙ share

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Medical device surveillance with electronic health records

Post-market medical device surveillance is a challenge facing manufactur...
04/03/2019 ∙ by Alison Callahan, et al. ∙ 0 ∙ share

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SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
03/29/2019 ∙ by Alexander Ratner, et al. ∙ 0 ∙ share

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Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Labeling training datasets has become a key barrier to building medical ...
03/26/2019 ∙ by Jared Dunnmon, et al. ∙ 12 ∙ share

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Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations

Fast linear transforms are ubiquitous in machine learning, including the...
03/14/2019 ∙ by Tri Dao, et al. ∙ 28 ∙ share

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Learning Dependency Structures for Weak Supervision Models

Labeling training data is a key bottleneck in the modern machine learnin...
03/14/2019 ∙ by Paroma Varma, et al. ∙ 8 ∙ share

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Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale

Labeling training data is one of the most costly bottlenecks in developi...
12/02/2018 ∙ by Stephen H. Bach, et al. ∙ 0 ∙ share

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Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation

We investigate how to train kernel approximation methods that generalize...
10/31/2018 ∙ by Jian Zhang, et al. ∙ 4 ∙ share

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Training Complex Models with Multi-Task Weak Supervision

As machine learning models continue to increase in complexity, collectin...
10/05/2018 ∙ by Alexander Ratner, et al. ∙ 14 ∙ share

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Learning Compressed Transforms with Low Displacement Rank

The low displacement rank (LDR) framework for structured matrices repres...
10/04/2018 ∙ by Anna T. Thomas, et al. ∙ 2 ∙ share

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Training Classifiers with Natural Language Explanations

Training accurate classifiers requires many labels, but each label provi...
05/10/2018 ∙ by Braden Hancock, et al. ∙ 0 ∙ share

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Representation Tradeoffs for Hyperbolic Embeddings

Hyperbolic embeddings offer excellent quality with few dimensions when e...
04/10/2018 ∙ by Christopher De Sa, et al. ∙ 0 ∙ share

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Hypertree Decompositions Revisited for PGMs

We revisit the classical problem of exact inference on probabilistic gra...
04/05/2018 ∙ by Aarthy Shivram Arun, et al. ∙ 0 ∙ share

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A Kernel Theory of Modern Data Augmentation

Data augmentation, a technique in which a training set is expanded with ...
03/16/2018 ∙ by Tri Dao, et al. ∙ 0 ∙ share

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High-Accuracy Low-Precision Training

Low-precision computation is often used to lower the time and energy cos...
03/09/2018 ∙ by Christopher De Sa, et al. ∙ 0 ∙ share

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A Formal Framework For Probabilistic Unclean Databases

Traditional modeling of inconsistency in database theory casts all possi...
01/21/2018 ∙ by Christopher De Sa, et al. ∙ 0 ∙ share

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Snorkel: Rapid Training Data Creation with Weak Supervision

Labeling training data is increasingly the largest bottleneck in deployi...
11/28/2017 ∙ by Alexander Ratner, et al. ∙ 0 ∙ share

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Inferring Generative Model Structure with Static Analysis

Obtaining enough labeled data to robustly train complex discriminative m...
09/07/2017 ∙ by Paroma Varma, et al. ∙ 0 ∙ share

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Christopher Ré

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