| 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 | 124 | 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 | 94 | 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 | 61 | 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 | 56 | 2022 |
| Comparing Acoustic-based Approaches for Alzheimer's Disease Detection A Balagopalan, J Novikova arXiv preprint arXiv:2106.01555, 2021 | 34 | 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 | 28 | 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 | 28 | 2018 |
| 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 | 12 | 2020 |
| 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 | 11 | 2023 |
| 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 | 10 | 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 | 9 | 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 | 8 | 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 | 6 | 2023 |
| Quantifying the Task-Specific Information in Text-Based Classifications Z Zhu, A Balagopalan, M Ghassemi, F Rudzicz arXiv preprint arXiv:2110.08931, 2021 | 3 | 2021 |
| Augmenting BERT Carefully with Underrepresented Linguistic Features A Balagopalan, J Novikova arXiv preprint arXiv:2011.06153, 2020 | 3 | 2020 |
| The Role of Relevance in Fair Ranking A Balagopalan, AZ Jacobs, A Biega arXiv preprint arXiv:2305.05608, 2023 | 2 | 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 | 2 | 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 | 2 | 2018 |
| 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 | | 2024 |
| Event-Based Contrastive Learning for Medical Time Series H Jeong, N Oufattole, A Balagopalan, M Mcdermott, P Chandak, ... arXiv preprint arXiv:2312.10308, 2023 | | 2023 |
Jekaterina NovikovaWinterlight Labs | Cambridge CognitionVerified email at winterlightlabs.com
