1 citations · 2 across the 10 of their papers we have counts for
10 papers
DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift
Yongsen Tan, Zhecheng Sheng, Xiruo Ding +2
Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-remains poorly understood an…
When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift
Zhecheng Sheng, Yongsen Tan, Xiruo Ding +2
In classification tasks, models may rely on confounding variables to achieve strong in-distribution performance, capturing spurious features that fail under distribution shift. Thi…
Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking
Zhecheng Sheng, Xiruo Ding, Brian Hur +3
Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models…
"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test
Changye Li, Zhecheng Sheng, Trevor Cohen +1
Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in…
Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder
Changye Li, Weizhe Xu, Serguei Pakhomov +3
Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently, clinical estimates of the severity of disorganized thinking have been shown to co…
Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance
Changye Li, Trevor Cohen, Serguei Pakhomov
Automatic speech recognition (ASR) models trained on large amounts of audio data are now widely used to convert speech to written text in a variety of applications from video capti…
Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies
Changye Li, Zhecheng Sheng, Trevor Cohen +1
As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. T…
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?
Changye Li, Weizhe Xu, Trevor Cohen +1
\textbf{Objectives}: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the ``Cookie Theft'…
Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts
Xiruo Ding, Zhecheng Sheng, Brian Hur +3
Foundation models are a current focus of attention in both industry and academia. While they have shown their capabilities in a variety of tasks, in-depth research is required to d…
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Xiruo Ding, Zhecheng Sheng, Meliha Yetişgen +2
Natural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of cl…