Search Results for author: Christopher Lee-Messer

Found 10 papers, 5 papers with code

ATCON: Attention Consistency for Vision Models

1 code implementation18 Oct 2022 Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel Rubin

We propose an unsupervised fine-tuning method that optimizes the consistency of attention maps and show that it improves both classification performance and the quality of attention maps.

Event Detection

TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring

no code implementations22 Jul 2022 Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer

We show that our sequential mistrust scores achieve high drift detection rates: over 90% of the streams show < 20% error for all domains.

Electroencephalogram (EEG) Seizure Detection

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

2 code implementations ICLR 2022 Sabri Eyuboglu, Maya Varma, Khaled Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Ré

In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1, 235 slice discovery settings in three input domains (natural images, medical images, and time-series data).

Representation Learning Time Series Analysis

Automated Detection of Patients in Hospital Video Recordings

no code implementations28 Nov 2021 Siddharth Sharma, Florian Dubost, Christopher Lee-Messer, Daniel Rubin

We evaluate an ImageNet pre-trained Mask R-CNN, a standard deep learning model for object detection, on the task of patient detection using our own curated dataset of 45 videos of hospital patients.

Electroencephalogram (EEG) object-detection +1

Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing

1 code implementation28 Nov 2020 Florian Dubost, Erin Hong, Nandita Bhaskhar, Siyi Tang, Daniel Rubin, Christopher Lee-Messer

We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.

BIG-bench Machine Learning Electroencephalogram (EEG) +2

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