2.2k citations · 4.4k across the 41 of their papers we have counts for
13 papers · 1 filter
Magnitude Invariant Parametrizations Improve Hypernetwork Learning
Jose Javier Gonzalez Ortiz, John Guttag, Adrian Dalca
Hypernetworks, neural networks that predict the parameters of another neural network, are powerful models that have been successfully used in diverse applications from image genera…
Improved Text Classification via Test-Time Augmentation
Helen Lu, Divya Shanmugam, Harini Suresh +1
Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classificatio…
Saliency Cards: A Framework to Characterize and Compare Saliency Methods
Angie Boggust, Harini Suresh, Hendrik Strobelt +2
Saliency methods are a common class of machine learning interpretability techniques that calculate how important each input feature is to a model's output. We find that, with the r…
Data Augmentation for Electrocardiograms
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev +2
Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to…
Multiplying Matrices Without Multiplying
Davis Blalock, John Guttag
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Consequently, there has been significant work on efficiently approximating…
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction
Aniruddh Raghu, John Guttag, Katherine Young +3
The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, w…
What is the State of Neural Network Pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle +1
Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years. We provide a meta-analysi…
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar, Fredrik D. Johansson, John Guttag +1
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often…
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Harini Suresh, John V. Guttag
As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable…
Multiple Instance Learning for ECG Risk Stratification
Divya Shanmugam, Davis Blalock, John Guttag
Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of…
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Harini Suresh, Jen J. Gong, John Guttag
Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heter…
Uncovering Voice Misuse Using Symbolic Mismatch
Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta +3
Voice disorders affect an estimated 14 million working-aged Americans, and many more worldwide. We present the first large scale study of vocal misuse based on long-term ambulatory…
Transferring Knowledge from Text to Predict Disease Onset
Yun Liu, Kun-Ta Chuang, Fu-Wen Liang +3
In many domains such as medicine, training data is in short supply. In such cases, external knowledge is often helpful in building predictive models. We propose a novel method to i…