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pro vyhledávání: '"Priyadarshi, Sweta"'
Autor:
Bhardwaj, Kartikeya, Pandey, Nilesh Prasad, Priyadarshi, Sweta, Ganapathy, Viswanath, Esteves, Rafael, Kadambi, Shreya, Borse, Shubhankar, Whatmough, Paul, Garrepalli, Risheek, Van Baalen, Mart, Teague, Harris, Nagel, Markus
In this paper, we propose Sparse High Rank Adapters (SHiRA) that directly finetune 1-2% of the base model weights while leaving others unchanged, thus, resulting in a highly sparse adapter. This high sparsity incurs no inference overhead, enables rap
Externí odkaz:
http://arxiv.org/abs/2407.16712
Autor:
Bhardwaj, Kartikeya, Pandey, Nilesh Prasad, Priyadarshi, Sweta, Ganapathy, Viswanath, Esteves, Rafael, Kadambi, Shreya, Borse, Shubhankar, Whatmough, Paul, Garrepalli, Risheek, Van Baalen, Mart, Teague, Harris, Nagel, Markus
Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models adding no overhead during inference. However, from a mobile deployment
Externí odkaz:
http://arxiv.org/abs/2406.13175
Autor:
Borse, Shubhankar, Kadambi, Shreya, Pandey, Nilesh Prasad, Bhardwaj, Kartikeya, Ganapathy, Viswanath, Priyadarshi, Sweta, Garrepalli, Risheek, Esteves, Rafael, Hayat, Munawar, Porikli, Fatih
While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated images, as the model tends to copy data from the observed training samples
Externí odkaz:
http://arxiv.org/abs/2406.08798
Autor:
Bhardwaj, Kartikeya, Pandey, Nilesh Prasad, Priyadarshi, Sweta, Lee, Kyunggeun, Ma, Jun, Teague, Harris
Large generative models such as large language models (LLMs) and diffusion models have revolutionized the fields of NLP and computer vision respectively. However, their slow inference, high computation and memory requirement makes it challenging to d
Externí odkaz:
http://arxiv.org/abs/2403.18159
Autor:
Priyadarshi, Sweta, Jiang, Tianyu, Cheng, Hsin-Pai, Krishna, Sendil, Ganapathy, Viswanath, Patel, Chirag
With the growing demand for vision applications and deployment across edge devices, the development of hardware-friendly architectures that maintain performance during device deployment becomes crucial. Neural architecture search (NAS) techniques exp
Externí odkaz:
http://arxiv.org/abs/2309.14670
Zero-Shot Neural Architecture Search (NAS) approaches propose novel training-free metrics called zero-shot proxies to substantially reduce the search time compared to the traditional training-based NAS. Despite the success on image classification, th
Externí odkaz:
http://arxiv.org/abs/2309.14666