Zobrazeno 1 - 10
of 9 663
pro vyhledávání: '"GAO, Chao"'
Autor:
Luo, Yuetian, Gao, Chao
This paper studies the construction of adaptive confidence intervals under Huber's contamination model when the contamination proportion is unknown. For the robust confidence interval of a Gaussian mean, we show that the optimal length of an adaptive
Externí odkaz:
http://arxiv.org/abs/2410.22647
This paper explores the problem of class-generalizable anomaly detection, where the objective is to train one unified AD model that can generalize to detect anomalies in diverse classes from different domains without any retraining or fine-tuning on
Externí odkaz:
http://arxiv.org/abs/2410.20047
We consider deep deterministic policy gradient (DDPG) in the context of reinforcement learning with sparse rewards. To enhance exploration, we introduce a search procedure, \emph{${\epsilon}{t}$-greedy}, which generates exploratory options for explor
Externí odkaz:
http://arxiv.org/abs/2410.05225
We consider testing the goodness-of-fit of a distribution against alternatives separated in sup norm. We study the twin settings of Poisson-generated count data with a large number of categories and high-dimensional multinomials. In previous studies
Externí odkaz:
http://arxiv.org/abs/2409.08871
In this paper, we consider partially observable timed automata endowed with a single clock. A time interval is associated with each transition specifying at which clock values it may occur. In addition, a resetting condition associated to a transitio
Externí odkaz:
http://arxiv.org/abs/2409.05810
We investigate the ground-state properties and quantum phase transitions of an ensemble consisting of $N$ four-level atoms within an optical cavity coupled to the single cavity mode and external laser fields. The system is described by an extended im
Externí odkaz:
http://arxiv.org/abs/2408.10121
This study evaluates the performance of Recurrent Neural Network (RNN) and Transformer models in replicating cross-language structural priming, a key indicator of abstract grammatical representations in human language processing. Focusing on Chinese-
Externí odkaz:
http://arxiv.org/abs/2405.09508
Publikováno v:
Phys. Rev. Lett. 133, 163401 (2024)
The work intends to extend the moir\'e physics to three dimensions. Three-dimensional moir\'e patterns can be realized in ultracold atomic gases by coupling two spin states in spin-dependent optical lattices with a relative twist, a structure current
Externí odkaz:
http://arxiv.org/abs/2404.19608
The translation symmetry of a lattice is greatly modified when subjected to a perpendicular magnetic field [Zak, Phys. Rev. \textbf{134}, A1602 (1964)]. This change in symmetry can lead to magnetic unit cells that are substantially larger than the or
Externí odkaz:
http://arxiv.org/abs/2404.08211
Autor:
Gao, Chao, Zhang, Sai Qian
To enhance the performance of large language models (LLM) on downstream tasks, one solution is to fine-tune certain LLM parameters and make it better align with the characteristics of the training dataset. This process is commonly known as parameter-
Externí odkaz:
http://arxiv.org/abs/2404.05182