2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models
Divij Gupta, Anubhav Bhatti, Surajsinh Parmar
Time Series Foundation Models (TSFMs) have recently garnered attention for their ability to model complex, large-scale time series data across domains such as retail, finance, and…
Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps
Yuwei Liu, Chen Dan, Anubhav Bhatti +4
Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict seps…
Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting
Divij Gupta, Anubhav Bhatti, Suraj Parmar +4
Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. However, its application to ti…
Extending Machine Learning-Based Early Sepsis Detection to Different Demographics
Surajsinh Parmar, Tao Shan, San Lee +2
Sepsis requires urgent diagnosis, but research is predominantly focused on Western datasets. In this study, we perform a comparative analysis of two ensemble learning methods, Ligh…
Investigating Poor Performance Regions of Black Boxes: LIME-based Exploration in Sepsis Detection
Mozhgan Salimiparsa, Surajsinh Parmar, San Lee +3
Interpreting machine learning models remains a challenge, hindering their adoption in clinical settings. This paper proposes leveraging Local Interpretable Model-Agnostic Explanati…