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of 48 796
pro vyhledávání: '"CHEN, Hong"'
Autor:
Yang, Shuo, Wang, Caojun, Zhang, Yuanjian, Yin, Yuming, Huang, Yanjun, Li, Shengbo Eben, Chen, Hong
Adversarial scenario generation is crucial for autonomous driving testing because it can efficiently simulate various challenge and complex traffic conditions. However, it is difficult to control current existing methods to generate desired scenarios
Externí odkaz:
http://arxiv.org/abs/2408.14000
Autonomous driving vehicles with self-learning capabilities are expected to evolve in complex environments to improve their ability to cope with different scenarios. However, most self-learning algorithms suffer from low learning efficiency and lacki
Externí odkaz:
http://arxiv.org/abs/2408.12190
Autonomous vehicles with a self-evolving ability are expected to cope with unknown scenarios in the real-world environment. Take advantage of trial and error mechanism, reinforcement learning is able to self evolve by learning the optimal policy, and
Externí odkaz:
http://arxiv.org/abs/2408.12187
Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios. However, as a result of the safety-performance trade-off mechanism of
Externí odkaz:
http://arxiv.org/abs/2408.12805
Instruct LLM provide a paradigm used in large scale language model to align LLM to human preference. The paradigm contains supervised fine tuning and reinforce learning from human feedback. This paradigm is also used in downstream scenarios to adapt
Externí odkaz:
http://arxiv.org/abs/2408.10642
Learning from human preference is a paradigm used in large-scale language model (LLM) fine-tuning step to better align pretrained LLM to human preference for downstream task. In the past it uses reinforcement learning from human feedback (RLHF) algor
Externí odkaz:
http://arxiv.org/abs/2408.09834
Autor:
Gong, Jiahui, Ding, Jingtao, Meng, Fanjin, Chen, Guilong, Chen, Hong, Zhao, Shen, Lu, Haisheng, Li, Yong
Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life. With the rise of generative AI services, smartphones can potentially transform into personalized assistants, anticipating us
Externí odkaz:
http://arxiv.org/abs/2408.09815
Autor:
Li, Yiyan, Li, Haoyang, Pu, Zhao, Zhang, Jing, Zhang, Xinyi, Ji, Tao, Sun, Luming, Li, Cuiping, Chen, Hong
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance. However, traditional tuning methods often follow a Try-Collect-Adjust approach, proving inefficient and database-specific. Moreover, these me
Externí odkaz:
http://arxiv.org/abs/2408.02213
The Sparse Vector Technique (SVT) is one of the most fundamental tools in differential privacy (DP). It works as a backbone for adaptive data analysis by answering a sequence of queries on a given dataset, and gleaning useful information in a privacy
Externí odkaz:
http://arxiv.org/abs/2407.20068
The shuffle model of Differential Privacy (DP) is an enhanced privacy protocol which introduces an intermediate trusted server between local users and a central data curator. It significantly amplifies the central DP guarantee by anonymizing and shuf
Externí odkaz:
http://arxiv.org/abs/2407.18157