3 papers
cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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…