10 papers
MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning
Zirui Cheng, Xun Xu, Tiankai Chen +7
Few-shot in-context learning (ICL) with multi-modal large language models (MLLMs) enables task adaptation without parameter updates, but its performance is highly sensitive to the…
Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads
Bharadwaj Veeravalli
In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectures for the Divisible Load Th…
CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning
Jingze Ge, Xue Geng, Yun Liu +6
To mitigate the memory constraints associated with fine-tuning large pre-trained models, existing parameter-efficient fine-tuning (PEFT) methods, such as LoRA, rely on low-rank upd…
Resource-Aware Task Allocator Design: Insights and Recommendations for Distributed Satellite Constellations
Bharadwaj Veeravalli
We present the design of a Resource-Aware Task Allocator (RATA) and an empirical analysis in handling real-time tasks for processing on Distributed Satellite Systems (DSS). We cons…
A Multi-Port Concurrent Communication Model for handling Compute Intensive Tasks on Distributed Satellite System Constellations
Bharadwaj Veeravalli
We develop an integrated Multi-Port Concurrent Communication Divisible Load Theory (MPCC-DLT) framework for relay-centric distributed satellite systems (DSS), capturing concurrent…
DART-ing Through the Drift: Dynamic Tracing of Knowledge Neurons for Adaptive Inference-Time Pruning
Abhishek Tyagi, Yunuo Cen, Shrey Dhorajiya +2
Large Language Models (LLMs) exhibit substantial parameter redundancy, particularly in Feed-Forward Networks (FFNs). Existing pruning methods suffer from two primary limitations. F…