Zobrazeno 1 - 10
of 1 060
pro vyhledávání: '"Zhao Xingyu"'
Autor:
LI Haodang, DING Zhen, ZHANG Kai, LUO Huiqiang, CAO Zhengyuan, CUI Wen, YOU Xiusong, ZHAO Xingyu, MENG Guangrui, SUN Jiang, DENG Wenge
Publikováno v:
Gong-kuang zidonghua, Vol 48, Iss 2, Pp 1-10 (2022)
Coal is the guarantee energy in China, and its dominant position in energy will not change for a period of time in the future. Coal intelligent mining will show a rapid development trend and enter into a new stage of development. Through induction an
Externí odkaz:
https://doaj.org/article/c5e295e884e04f89a379fd42a38fa403
Autor:
Wu, Sihao, Liu, Jiaxu, Yin, Xiangyu, Cheng, Guangliang, Zhao, Xingyu, Fang, Meng, Yi, Xinping, Huang, Xiaowei
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of purely data-driven methods. Current LLM-based agents require lengthy infe
Externí odkaz:
http://arxiv.org/abs/2410.12568
Autor:
Zhang, Yi, Chen, Zhen, Cheng, Chih-Hong, Ruan, Wenjie, Huang, Xiaowei, Zhao, Dezong, Flynn, David, Khastgir, Siddartha, Zhao, Xingyu
Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of
Externí odkaz:
http://arxiv.org/abs/2409.18214
The vulnerability of machine learning models to Membership Inference Attacks (MIAs) has garnered considerable attention in recent years. These attacks determine whether a data sample belongs to the model's training set or not. Recent research has foc
Externí odkaz:
http://arxiv.org/abs/2409.00426
Data efficiency of learning, which plays a key role in the Reinforcement Learning (RL) training process, becomes even more important in continual RL with sequential environments. In continual RL, the learner interacts with non-stationary, sequential
Externí odkaz:
http://arxiv.org/abs/2408.13452
The deployment of generative AI (GenAI) models raises significant fairness concerns, addressed in this paper through novel characterization and enforcement techniques specific to GenAI. Unlike standard AI performing specific tasks, GenAI's broad func
Externí odkaz:
http://arxiv.org/abs/2404.16663
Autor:
Hou, Waner, Zhao, Xingyu, Rehan, Kamran, Li, Yi, Li, Yue, Lutz, Eric, Lin, Yiheng, Du, Jiangfeng
Quantum friction, a quantum analog of classical friction, reduces the performance of quantum machines, such as heat engines, and makes them less energy efficient. We here report the experimental realization of an energy efficient quantum engine coupl
Externí odkaz:
http://arxiv.org/abs/2404.15075
Autor:
Zhang, Yi, Tang, Yun, Ruan, Wenjie, Huang, Xiaowei, Khastgir, Siddartha, Jennings, Paul, Zhao, Xingyu
Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in generating high-quality images based on simple text descriptions. However, as is common with many Deep Learning (DL) models, DMs are subject to a lack of robustness. While
Externí odkaz:
http://arxiv.org/abs/2402.15429
Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection. We focus on its role in safet
Externí odkaz:
http://arxiv.org/abs/2402.07031
Autor:
Dong, Yi, Mu, Ronghui, Jin, Gaojie, Qi, Yi, Hu, Jinwei, Zhao, Xingyu, Meng, Jie, Ruan, Wenjie, Huang, Xiaowei
As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts on human users and societies. Guardrails, which filter the inputs or
Externí odkaz:
http://arxiv.org/abs/2402.01822