activity
20192024
most citedAutoML using Metadata Language Embeddings

17 citations · 22 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.NC2024

EEG-estimated functional connectivity, and not behavior, differentiates Parkinson's patients from health controls during the Simon conflict task

Xiaoxiao Sun, Chongkun Zhao, Sharath Koorathota +1

Neural biomarkers that can classify or predict disease are of broad interest to the neurological and psychiatric communities. Such biomarkers can be informative of disease state or…

cs.CV2023★ 3 cited

Gaze-Informed Vision Transformers: Predicting Driving Decisions Under Uncertainty

Sharath Koorathota, Nikolas Papadopoulos, Jia Li Ma +5

Vision Transformers (ViT) have advanced computer vision, yet their efficacy in complex tasks like driving remains less explored. This study enhances ViT by integrating human eye ga…

cs.LG2021★ 1 cited

Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data

Sharath Koorathota, Arunesh Mittal, Richard P. Sloan +1

Cognition in midlife is an important predictor of age-related mental decline and statistical models that predict cognitive performance can be useful for predicting decline. However…

eess.SP2020★ 1 cited

Amark: Automated Marking and Processing Techniques for Ambulatory ECG Data

Sharath Koorathota, Richard P. Sloan

We describe techniques and specifications of MATLAB software to process ambulatory electrocardiogram (ECG) data. Through template-based beat identification and simple pattern recog…

cs.LG2019★ 17 cited

AutoML using Metadata Language Embeddings

Iddo Drori, Lu Liu, Yi Nian +5

As a human choosing a supervised learning algorithm, it is natural to begin by reading a text description of the dataset and documentation for the algorithms you might use. We demo…