64 citations · 147 across the 44 of their papers we have counts for
6 papers · 1 filter
MaskVD: Region Masking for Efficient Video Object Detection
Sreetama Sarkar, Gourav Datta, Souvik Kundu +3
Video tasks are compute-heavy and thus pose a challenge when deploying in real-time applications, particularly for tasks that require state-of-the-art Vision Transformers (ViTs). S…
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation
Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
Low-rank adaptation (LoRA) has become the default approach to fine-tune large language models (LLMs) due to its significant reduction in trainable parameters. However, trainable pa…
Block Selective Reprogramming for On-device Training of Vision Transformers
Sreetama Sarkar, Souvik Kundu, Kai Zheng +1
The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with…
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models
Zeyu Liu, Souvik Kundu, Anni Li +3
We present a novel Parameter-Efficient Fine-Tuning (PEFT) method, dubbed as Adaptive Freezing of Low Rank Adaptation (AFLoRA). Specifically, for each pre-trained frozen weight tens…
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM
Hao Kang, Qingru Zhang, Souvik Kundu +4
Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference. However, the growing cache demand with increasing sequence…
Linearizing Models for Efficient yet Robust Private Inference
Sreetama Sarkar, Souvik Kundu, Peter A. Beerel
The growing concern about data privacy has led to the development of private inference (PI) frameworks in client-server applications which protects both data privacy and model IP.…