Zobrazeno 1 - 10
of 109 178
pro vyhledávání: '"WANG, Jing"'
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensive recommendation logic generation. However, in existing related studie
Externí odkaz:
http://arxiv.org/abs/2407.02833
Autor:
Liu, Jian, Wu, Jianyu, Xie, Hairun, Zhang, Guoqing, Wang, Jing, Liu, Wei, Ouyang, Wanli, Jiang, Junjun, Liu, Xianming, Tang, Shixiang, Zhang, Miao
Data-driven generative models have emerged as promising approaches towards achieving efficient mechanical inverse design. However, due to prohibitively high cost in time and money, there is still lack of open-source and large-scale benchmarks in this
Externí odkaz:
http://arxiv.org/abs/2406.18846
The expansion of model parameters underscores the significance of pre-trained models; however, the constraints encountered during model deployment necessitate models of variable sizes. Consequently, the traditional pre-training and fine-tuning paradi
Externí odkaz:
http://arxiv.org/abs/2406.17503
Autor:
Qian, Dongheng, Wang, Jing
Measurement-induced phase transition (MIPT) is a novel non-equilibrium phase transition characterized by entanglement entropy. The scrambling dynamics induced by random unitary gates can protect information from low-rate measurements. However, common
Externí odkaz:
http://arxiv.org/abs/2406.14109
The fast algorithms in Fourier optics have invigorated multifunctional device design and advanced imaging technologies. However, the necessity for fast computations has led to limitations in the widely used conventional Fourier methods, manifesting a
Externí odkaz:
http://arxiv.org/abs/2406.15456
Autor:
Zhang, Qiao-Chu, Wang, Jing
The effects of short-range fermion-fermion interactions on the low-energy properties of the rhombohedral trilayer graphene are comprehensively investigated by virtue of the momentum-shell renormalization group method. We take into account all one-loo
Externí odkaz:
http://arxiv.org/abs/2406.01877
The use of machine learning methods for predicting the properties of crystalline materials encounters significant challenges, primarily related to input encoding, output versatility, and interpretability. Here, we introduce CrystalBERT, an adaptable
Externí odkaz:
http://arxiv.org/abs/2405.18944
We study the probability distribution of the resistance, or equivalently the charge transmission, of a decoherent quantum Hall-superconductor edge, with the decoherence coming from metallic puddles along the edge. Such metallic puddles may originate
Externí odkaz:
http://arxiv.org/abs/2405.17550
In this paper, we introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning, which arise from dependencies on instances and labels. We start b
Externí odkaz:
http://arxiv.org/abs/2405.16474
LiDAR sensors play a crucial role in various applications, especially in autonomous driving. Current research primarily focuses on optimizing perceptual models with point cloud data as input, while the exploration of deeper cognitive intelligence rem
Externí odkaz:
http://arxiv.org/abs/2405.15274