Search Results for author: Andrew L. Beam

Found 10 papers, 7 papers with code

TIER: Text-Image Entropy Regularization for CLIP-style models

1 code implementation13 Dec 2022 Anil Palepu, Andrew L. Beam

We formalize this observation using a novel regularization scheme that penalizes the entropy of the text-token to image-patch similarity scores.

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction

1 code implementation19 May 2022 Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew L. Beam

In this work, we introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding using proximal inference.

MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning

1 code implementation26 Mar 2021 Akshay Smit, Damir Vrabac, Yujie He, Andrew Y. Ng, Andrew L. Beam, Pranav Rajpurkar

We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources.

General Classification Image Classification +3

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data

no code implementations2 Oct 2020 David Bellamy, Leo Celi, Andrew L. Beam

The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision.

BIG-bench Machine Learning Decompensation +1

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018

no code implementations17 Nov 2018 Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi

This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.

BIG-bench Machine Learning

Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes

1 code implementation3 Nov 2018 Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam

Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes.

BIG-bench Machine Learning

Adversarial Attacks Against Medical Deep Learning Systems

1 code implementation15 Apr 2018 Samuel G. Finlayson, Hyung Won Chung, Isaac S. Kohane, Andrew L. Beam

The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems.

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

4 code implementations4 Apr 2018 Andrew L. Beam, Benjamin Kompa, Allen Schmaltz, Inbar Fried, Griffin Weber, Nathan P. Palmer, Xu Shi, Tianxi Cai, Isaac S. Kohane

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing.

Word Embeddings

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

1 code implementation15 Apr 2014 Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle

Discovering causal genetic variants from large genetic association studies poses many difficult challenges.

Computational Efficiency

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