3 papers
cs.DC2026
Koi: Evidence-Guided, Self-Calibrating Planning for Multi-Cloud, Heterogeneous LLM Inference Fleets
Mankeerat Sidhu, Gangmuk Lim, Hetarth Chopra +10
Deploying a large language model (LLM) requires choosing the right accelerator, parallelization strategy, engine configuration, and replica count amongst ~140 other variables acros…
cs.LG2025
LEWIS (LayEr WIse Sparsity) -- A Training Free Guided Model Merging Approach
Hetarth Chopra, Vidhi Rambhia, Vikram Adve
As specialized large language models (LLMs) become increasingly prevalent, model merging methods are being used to combine them to create a single multi-task model without requirin…
cs.CV2024
Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval
Mankeerat Sidhu, Hetarth Chopra, Ansel Blume +3
In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retr…