Zobrazeno 1 - 10
of 456
pro vyhledávání: '"P A Leye"'
This paper constructs question answering system for bridge design specification based on large language model. Three implementation schemes are tried: full fine-tuning of the Bert pretrained model, parameter-efficient fine-tuning of the Bert pretrain
Externí odkaz:
http://arxiv.org/abs/2408.13282
Autor:
Liu, Ji, Jia, Juncheng, Zhang, Hong, Yun, Yuhui, Wang, Leye, Zhou, Yang, Dai, Huaiyu, Dou, Dejing
Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this paper, we propose a new FL framework, i.e., FedDUMAP, with three original
Externí odkaz:
http://arxiv.org/abs/2408.05678
Deep Q-network algorithm is used to select economic span of bridge. Selection of bridge span has a significant impact on the total cost of bridge, and a reasonable selection of span can reduce engineering cost. Economic span of bridge is theoreticall
Externí odkaz:
http://arxiv.org/abs/2407.06507
Learning representations of user behavior sequences is crucial for various online services, such as online fraudulent transaction detection mechanisms. Graph Neural Networks (GNNs) have been extensively applied to model sequence relationships, and ex
Externí odkaz:
http://arxiv.org/abs/2406.02979
Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price
Externí odkaz:
http://arxiv.org/abs/2405.14893
Attempt to use convolutional neural network to achieve kinematic analysis of plane bar structure. Through 3dsMax animation software and OpenCV module, self-build image dataset of geometrically stable system and geometrically unstable system. we const
Externí odkaz:
http://arxiv.org/abs/2405.02807
Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL facilitates collaboration among multiple entities with distinct feature
Externí odkaz:
http://arxiv.org/abs/2405.02364
Self-supervised learning shows promise in harnessing extensive unlabeled data, but it also confronts significant privacy concerns, especially in vision. In this paper, we aim to perform membership inference on visual self-supervised models in a more
Externí odkaz:
http://arxiv.org/abs/2404.02462
Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing. However, existing ST models often require region partition as a prerequisite, resulting in two main pitfalls. Firstly, lo
Externí odkaz:
http://arxiv.org/abs/2403.07022
Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation, such as uniform node dropping
Externí odkaz:
http://arxiv.org/abs/2401.17580