Zobrazeno 1 - 10
of 1 708
pro vyhledávání: '"Yang, Yuxin"'
Autor:
Suryanarayanan, Parthasarathy, Qiu, Yunguang, Sethi, Shreyans, Mahajan, Diwakar, Li, Hongyang, Yang, Yuxin, Eyigoz, Elif, Saenz, Aldo Guzman, Platt, Daniel E., Rumbell, Timothy H., Ng, Kenney, Dey, Sanjoy, Burch, Myson, Kwon, Bum Chul, Meyer, Pablo, Cheng, Feixiong, Hu, Jianying, Morrone, Joseph A.
Foundation models applied to bio-molecular space hold promise to accelerate drug discovery. Molecular representation is key to building such models. Previous works have typically focused on a single representation or view of the molecules. Here, we d
Externí odkaz:
http://arxiv.org/abs/2410.19704
Federated graph learning (FedGL) is an emerging learning paradigm to collaboratively train graph data from various clients. However, during the development and deployment of FedGL models, they are susceptible to illegal copying and model theft. Backd
Externí odkaz:
http://arxiv.org/abs/2410.17533
Federated learning (FL) is an emerging distributed learning paradigm without sharing participating clients' private data. However, existing works show that FL is vulnerable to both Byzantine (security) attacks and data reconstruction (privacy) attack
Externí odkaz:
http://arxiv.org/abs/2407.19703
Federated Learning (FL) is a novel client-server distributed learning framework that can protect data privacy. However, recent works show that FL is vulnerable to poisoning attacks. Many defenses with robust aggregators (AGRs) are proposed to mitigat
Externí odkaz:
http://arxiv.org/abs/2407.15267
Federated graph learning (FedGL) is an emerging federated learning (FL) framework that extends FL to learn graph data from diverse sources. FL for non-graph data has shown to be vulnerable to backdoor attacks, which inject a shared backdoor trigger i
Externí odkaz:
http://arxiv.org/abs/2407.08935
Autor:
Huang, Yiyang, Hao, Yuhui, Yu, Bo, Yan, Feng, Yang, Yuxin, Min, Feng, Han, Yinhe, Ma, Lin, Liu, Shaoshan, Liu, Qiang, Gan, Yiming
Embodied AI robots have the potential to fundamentally improve the way human beings live and manufacture. Continued progress in the burgeoning field of using large language models to control robots depends critically on an efficient computing substra
Externí odkaz:
http://arxiv.org/abs/2407.04292
Test generation has been a critical and labor-intensive process in hardware design verification. Recently, the emergence of Large Language Model (LLM) with their advanced understanding and inference capabilities, has introduced a novel approach. In t
Externí odkaz:
http://arxiv.org/abs/2406.04373
As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmentation (DA). Conducting DA in private domain requires private protecti
Externí odkaz:
http://arxiv.org/abs/2402.16515
Human trajectory forecasting is a critical challenge in fields such as robotics and autonomous driving. Due to the inherent uncertainty of human actions and intentions in real-world scenarios, various unexpected occurrences may arise. To uncover late
Externí odkaz:
http://arxiv.org/abs/2401.02916
Current object re-identification (ReID) system follows the centralized processing paradigm, i.e., all computations are conducted in the cloud server and edge devices are only used to capture and send images. As the number of videos experiences a rapi
Externí odkaz:
http://arxiv.org/abs/2401.02041