Zobrazeno 1 - 10
of 1 168
pro vyhledávání: '"ZHOU Zhi-Hua"'
Autor:
Yan Xian-rang, Hong Ming-fan, Zhou Zhi-hua, Liu Ai-qun, Peng Zhong-xing, Wu Wei-feng, Jing Cheng, Lin Jia-xiu, Long Ying, Yu Qing-yun
Publikováno v:
Translational Neuroscience, Vol 13, Iss 1, Pp 116-119 (2022)
We report a 30-year-old man involving gastrointestinal symptoms, vitreous opacity, and multiple cranial neuropathies. Transthyretin-related hereditary amyloidosis genetic testing revealed a rare c.251T > C variant p.(Phe84Ser). Only four cases with t
Externí odkaz:
https://doaj.org/article/e20b8e3a35f8462fa5d57ce40015fc99
Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. However, for complex learning targets, NeSy systems o
Externí odkaz:
http://arxiv.org/abs/2412.08457
Autor:
Zhang, Zhilong, Chen, Ruifeng, Ye, Junyin, Sun, Yihao, Wang, Pengyuan, Pang, Jingcheng, Li, Kaiyuan, Liu, Tianshuo, Lin, Haoxin, Yu, Yang, Zhou, Zhi-Hua
World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. To facilitate effective decision-making, world models must be equipped with strong
Externí odkaz:
http://arxiv.org/abs/2411.05619
We study episodic linear mixture MDPs with the unknown transition and adversarial rewards under full-information feedback, employing dynamic regret as the performance measure. We start with in-depth analyses of the strengths and limitations of the tw
Externí odkaz:
http://arxiv.org/abs/2411.03107
Gradient-variation online learning aims to achieve regret guarantees that scale with variations in the gradients of online functions, which has been shown to be crucial for attaining fast convergence in games and robustness in stochastic optimization
Externí odkaz:
http://arxiv.org/abs/2408.09074
Identifying causal relations is crucial for a variety of downstream tasks. In additional to observational data, background knowledge (BK), which could be attained from human expertise or experiments, is usually introduced for uncovering causal relati
Externí odkaz:
http://arxiv.org/abs/2407.15259
We study a new class of MDPs that employs multinomial logit (MNL) function approximation to ensure valid probability distributions over the state space. Despite its benefits, introducing the non-linear function raises significant challenges in both c
Externí odkaz:
http://arxiv.org/abs/2405.17061
We study reinforcement learning with linear function approximation, unknown transition, and adversarial losses in the bandit feedback setting. Specifically, we focus on linear mixture MDPs whose transition kernel is a linear mixture model. We propose
Externí odkaz:
http://arxiv.org/abs/2403.04568
Autor:
Tan, Zhi-Hao, Liu, Jian-Dong, Bi, Xiao-Dong, Tan, Peng, Zheng, Qin-Cheng, Liu, Hai-Tian, Xie, Yi, Zou, Xiao-Chuan, Yu, Yang, Zhou, Zhi-Hua
The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse numerous existing well-trained models instead of building machine learning models from scratch, with the hope of solving new user tasks even beyond models' original purposes
Externí odkaz:
http://arxiv.org/abs/2401.14427