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03/13/2023
Speech Intelligibility Classifiers from 550k Disordered Speech Samples
We developed dysarthric speech intelligibility classifiers on 551,176 di...
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03/10/2023
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings
Automatic Speech Recognition (ASR) in medical contexts has the potential...
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09/21/2022
Assessing ASR Model Quality on Disordered Speech using BERTScore
Word Error Rate (WER) is the primary metric used to assess automatic spe...
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07/27/2022
Is Attention All NeRF Needs?
We present Generalizable NeRF Transformer (GNT), a pure, unified transfo...
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05/08/2022
Context-Aware Abbreviation Expansion Using Large Language Models
Motivated by the need for accelerating text entry in augmentative and al...
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03/01/2022
TRILLsson: Distilled Universal Paralinguistic Speech Representations
Recent advances in self-supervision have dramatically improved the quali...
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07/23/2021
Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images
We analyze a dataset of retinal images using linear probes: linear regre...
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07/08/2021
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Automatic classification of disordered speech can provide an objective t...
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06/17/2021
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
Integrated Gradients (IG) is a commonly used feature attribution method ...
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08/10/2020
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
Diabetic retinopathy (DR) screening is instrumental in preventing blindn...
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07/10/2020
Scientific Discovery by Generating Counterfactuals using Image Translation
Model explanation techniques play a critical role in understanding the s...
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06/29/2020
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers ...
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12/12/2019
It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets
Confounding variables are a well known source of nuisance in biomedical ...
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10/18/2018
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Diabetic eye disease is one of the fastest growing causes of preventable...
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03/03/2017
Detecting Cancer Metastases on Gigapixel Pathology Images
Each year, the treatment decisions for more than 230,000 breast cancer p...
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08/30/2016
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions
Image segmentation from referring expressions is a joint vision and lang...
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06/24/2016
Captioning Images with Diverse Objects
Recent captioning models are limited in their ability to scale and descr...
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04/06/2016
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
This paper investigates how linguistic knowledge mined from large text c...
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11/17/2015
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
While recent deep neural network models have achieved promising results ...
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05/03/2015
Sequence to Sequence -- Video to Text
Real-world videos often have complex dynamics; and methods for generatin...
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12/15/2014
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
Solving the visual symbol grounding problem has long been a goal of arti...
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11/17/2014