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pro vyhledávání: '"Yunfeng"'
Autor:
Allen, Sarah M., author
Publikováno v:
Memory in Medieval China: Text, Ritual, and Community. 140:213-242
Autor:
Long, Yongzhen, Lu, Anhuai, Gu, Xiangping, Chi, Guoxiang, Ye, Lin, Jin, Zhongguo, Zhang, Dongliang
Publikováno v:
In Ore Geology Reviews February 2020 117
Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model t
Externí odkaz:
http://arxiv.org/abs/2409.04208
Autor:
Wang, Evan, Cassano, Federico, Wu, Catherine, Bai, Yunfeng, Song, Will, Nath, Vaskar, Han, Ziwen, Hendryx, Sean, Yue, Summer, Zhang, Hugh
While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute has not yet yielded analogous gains. We hypothesize that a core missing component is a lack of diverse LLM outputs, leading t
Externí odkaz:
http://arxiv.org/abs/2409.03733
Reinforcement Learning (RL) is highly dependent on the meticulous design of the reward function. However, accurately assigning rewards to each state-action pair in Long-Term RL (LTRL) challenges is formidable. Consequently, RL agents are predominantl
Externí odkaz:
http://arxiv.org/abs/2409.03301
In the domain of machine learning and game theory, the quest for Nash Equilibrium (NE) in extensive-form games with incomplete information is challenging yet crucial for enhancing AI's decision-making support under varied scenarios. Traditional Count
Externí odkaz:
http://arxiv.org/abs/2409.02706
Skeletal sequences, as well-structured representations of human behaviors, are crucial in Human Activity Recognition (HAR). The transferability of adversarial skeletal sequences enables attacks in real-world HAR scenarios, such as autonomous driving,
Externí odkaz:
http://arxiv.org/abs/2409.02483
Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving as a powerful alternative to reinforcement learning for exploratory co
Externí odkaz:
http://arxiv.org/abs/2408.06920
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous dense vector space by preserving the underlying structural properties o
Externí odkaz:
http://arxiv.org/abs/2408.02705
Autor:
Shi, Yunfeng, Wen, Li
We establish quantitative Green's function estimates for a class of quasi-periodic (QP) operators on $\mathbb{Z}^d$ with power-law long-range hopping and analytic cosine type potentials. As applications, we prove the arithmetic version of localizatio
Externí odkaz:
http://arxiv.org/abs/2408.01913