Zobrazeno 1 - 10
of 14 468
pro vyhledávání: '"Zhang, Qin"'
Autor:
Zhang, Zhan, Zhang, Qin, Jiao, Yang, Lu, Lin, Ma, Lin, Liu, Aihua, Liu, Xiao, Zhao, Juan, Xue, Yajun, Wei, Bing, Zhang, Mingxia, Gao, Ru, Zhao, Hong, Lu, Jie, Li, Fan, Zhang, Yang, Wang, Yiming, Zhang, Lei, Tian, Fengwei, Hu, Jie, Gou, Xin
Publikováno v:
Artificaial Intelligence Review, (2024) 57:151
AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, a
Externí odkaz:
http://arxiv.org/abs/2406.05746
High-speed railway stations are crucial junctions in high-speed railway networks. Compared to operations on the tracks between stations, trains have more routing possibilities within stations. As a result, track allocation at a station is relatively
Externí odkaz:
http://arxiv.org/abs/2405.01438
The interest in updating Large Language Models (LLMs) without retraining from scratch is substantial, yet it comes with some challenges.This is especially true for situations demanding complex reasoning with limited samples, a scenario we refer to as
Externí odkaz:
http://arxiv.org/abs/2403.15736
Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the comp
Externí odkaz:
http://arxiv.org/abs/2402.18495
Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain. It motivates us to study the unsupervised multiple-choice question answering (MCQA) problem. In t
Externí odkaz:
http://arxiv.org/abs/2402.17333
Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structura
Externí odkaz:
http://arxiv.org/abs/2402.16374
We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method is orthogonal to existing complicated methods to further enhance LLMs, while the d
Externí odkaz:
http://arxiv.org/abs/2402.05120
The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents. However, the substantial cost on maintaining the prolonged agent interactions poses challenge over the deployment of believable
Externí odkaz:
http://arxiv.org/abs/2402.02053
Recently, the cross-modal pretraining model has been employed to produce meaningful pseudo-labels to supervise the training of an image clustering model. However, numerous erroneous alignments in a cross-modal pre-training model could produce poor-qu
Externí odkaz:
http://arxiv.org/abs/2401.11740
Portfolio underdiversification is one of the most costly losses accumulated over a household's life cycle. We provide new evidence on the impact of financial inclusion services on households' portfolio choice and investment efficiency using 2015, 201
Externí odkaz:
http://arxiv.org/abs/2311.01206