Aparna Balagopalan
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To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
A Balagopalan, B Eyre, F Rudzicz, J Novikova
arXiv preprint arXiv:2008.01551, 2020
Visualizations of Deep Neural Networks in Computer Vision: A Survey
C Seifert, A Aamir, A Balagopalan, D Jain, A Sharma, S Grottel, ...
Transparent Data Mining for Big and Small Data, 123-144, 2017
Comparing Pre-trained and Feature-based Models for Prediction of Alzheimer’s Disease Based on Speech
A Balagopalan, B Eyre, J Robin, F Rudzicz, J Novikova
Frontiers in Aging Neuroscience 13, 189, 2021
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
A Balagopalan, H Zhang, K Hamidieh, T Hartvigsen, F Rudzicz, ...
arXiv preprint arXiv:2205.03295, 2022
Comparing Acoustic-based Approaches for Alzheimer's Disease Detection
A Balagopalan, J Novikova
arXiv preprint arXiv:2106.01555, 2021
Mitigating the impact of biased artificial intelligence in emergency decision-making
H Adam, A Balagopalan, E Alsentzer, F Christia, M Ghassemi
Communications Medicine 2 (1), 1-6, 2022
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
A Balagopalan, J Novikova, F Rudzicz, M Ghassemi
arXiv preprint arXiv:1811.12254, 2018
Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data
A Balagopalan, D Madras, DH Yang, D Hadfield-Menell, GK Hadfield, ...
Science Advances 9 (19), eabq0701, 2023
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
A Balagopalan, J Novikova, MBA McDermott, B Nestor, T Naumann, ...
Machine Learning for Health Workshop, 202-219, 2020
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
J Novikova, A Balagopalan, K Shkaruta, F Rudzicz
arXiv preprint arXiv:1910.00065, 2019
Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others
A Balagopalan, K Shkaruta, J Novikova
arXiv preprint arXiv:1904.01684, 2019
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach
B Eyre, A Balagopalan, J Novikova
arXiv preprint arXiv:2010.06579, 2020
Exploring the Use of Natural Language Processing for Objective Assessment of Disorganized Speech in Schizophrenia
L Jeong, M Lee, B Eyre, A Balagopalan, F Rudzicz, C Gabilondo
Psychiatric Research and Clinical Practice, 2023
Quantifying the Task-Specific Information in Text-Based Classifications
Z Zhu, A Balagopalan, M Ghassemi, F Rudzicz
arXiv preprint arXiv:2110.08931, 2021
Augmenting BERT Carefully with Underrepresented Linguistic Features
A Balagopalan, J Novikova
arXiv preprint arXiv:2011.06153, 2020
The Role of Relevance in Fair Ranking
A Balagopalan, AZ Jacobs, A Biega
arXiv preprint arXiv:2305.05608, 2023
S44. Using acoustic and linguistic markers from spontaneous speech to predict scores on the Montreal Cognitive Assessment (MoCA)
A Balagopalan, M Yancheva, J Novikova, W Simpson
Biological Psychiatry 85 (10), S313, 2019
ReGAN: RE [LAX| BAR| INFORCE] based Sequence Generation using GANs
A Balagopalan, S Gorti, M Ravaut, R Saqur
arXiv preprint arXiv:1805.02788, 2018
Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact
A Balagopalan, I Baldini, LA Celi, J Gichoya, LG McCoy, T Naumann, ...
PLOS Digital Health 3 (4), e0000474, 2024
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
H Jeong, S Jabbour, Y Yang, R Thapta, H Mozannar, WJ Han, ...
arXiv preprint arXiv:2403.01628, 2024
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