2 citations · 3 across the 7 of their papers we have counts for
7 papers
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…
Towards Democratizing Multilingual Large Language Models For Medicine Through A Two-Stage Instruction Fine-tuning Approach
Meng Zhou, Surajsinh Parmar, Anubhav Bhatti
Open-source, multilingual medical large language models (LLMs) have the potential to serve linguistically diverse populations across different regions. Adapting generic LLMs for he…
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…
SM70: A Large Language Model for Medical Devices
Anubhav Bhatti, Surajsinh Parmar, San Lee
We are introducing SM70, a 70 billion-parameter Large Language Model that is specifically designed for SpassMed's medical devices under the brand name 'JEE1' (pronounced as G1 and…
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…