Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Kompella, Ramana Rao"'
Autor:
Khalili, Hossein, Park, Seongbin, Li, Vincent, Bright, Brandan, Payani, Ali, Kompella, Ramana Rao, Sehatbakhsh, Nader
Autonomous mobile systems increasingly rely on deep neural networks for perception and decision-making. While effective, these systems are vulnerable to adversarial machine learning attacks where minor input perturbations can significantly impact out
Externí odkaz:
http://arxiv.org/abs/2409.00340
Machine learning models have been exponentially growing in terms of their parameter size over the past few years. We are now seeing the rise of trillion-parameter models. The large models cannot fit into a single GPU and thus require partitioned depl
Externí odkaz:
http://arxiv.org/abs/2407.08980
Autor:
Ji, Jiabao, Liu, Yujian, Zhang, Yang, Liu, Gaowen, Kompella, Ramana Rao, Liu, Sijia, Chang, Shiyu
As Large Language Models (LLMs) demonstrate extensive capability in learning from documents, LLM unlearning becomes an increasingly important research area to address concerns of LLMs in terms of privacy, copyright, etc. A conventional LLM unlearning
Externí odkaz:
http://arxiv.org/abs/2406.08607
Multi-task learning for dense prediction has emerged as a pivotal area in computer vision, enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks. However, the substantial computational demands of state-of-the-art (S
Externí odkaz:
http://arxiv.org/abs/2405.14136
Federated Learning (FL) emerged as a practical approach to training a model from decentralized data. The proliferation of FL led to the development of numerous FL algorithms and mechanisms. Many prior efforts have given their primary focus on accurac
Externí odkaz:
http://arxiv.org/abs/2403.17287
Generating unbounded 3D scenes is crucial for large-scale scene understanding and simulation. Urban scenes, unlike natural landscapes, consist of various complex man-made objects and structures such as roads, traffic signs, vehicles, and buildings. T
Externí odkaz:
http://arxiv.org/abs/2403.11697
The photonic platform holds great promise for quantum computing. Nevertheless, the intrinsic probabilistic characteristics of its native fusion operations introduces substantial randomness into the computing process, posing significant challenges to
Externí odkaz:
http://arxiv.org/abs/2403.01829
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
Autor:
Zhang, Yihua, Fan, Chongyu, Zhang, Yimeng, Yao, Yuguang, Jia, Jinghan, Liu, Jiancheng, Zhang, Gaoyuan, Liu, Gaowen, Kompella, Ramana Rao, Liu, Xiaoming, Liu, Sijia
The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications. However, they have also raised significant societal concerns, such as the g
Externí odkaz:
http://arxiv.org/abs/2402.11846
Autor:
Jin, Xin, Katsis, Charalampos, Sang, Fan, Sun, Jiahao, Bertino, Elisa, Kompella, Ramana Rao, Kundu, Ashish
The rampant occurrence of cybersecurity breaches imposes substantial limitations on the progress of network infrastructures, leading to compromised data, financial losses, potential harm to individuals, and disruptions in essential services. The curr
Externí odkaz:
http://arxiv.org/abs/2312.13119
Autor:
Pan, Zhuoshi, Yao, Yuguang, Liu, Gaowen, Shen, Bingquan, Zhao, H. Vicky, Kompella, Ramana Rao, Liu, Sijia
While state-of-the-art diffusion models (DMs) excel in image generation, concerns regarding their security persist. Earlier research highlighted DMs' vulnerability to data poisoning attacks, but these studies placed stricter requirements than convent
Externí odkaz:
http://arxiv.org/abs/2311.02373