Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Lyu, Shengfei"'
Ground Penetrating Radar (GPR) is widely used as a non-destructive approach to estimate buried utilities. When the GPR's detecting direction is perpendicular to a pipeline, a hyperbolic characteristic would be formed on the GPR B-scan image. However,
Externí odkaz:
http://arxiv.org/abs/2201.10184
With the rapid expansion of urban areas and the increasingly use of electricity, the need for locating buried cables is becoming urgent. In this paper, a noval method to locate underground cables based on Ground Penetrating Radar (GPR) and Gaussian-p
Externí odkaz:
http://arxiv.org/abs/2201.11253
Deep neural networks such as BERT have made great progress in relation classification. Although they can achieve good performance, it is still a question of concern whether these models recognize the directionality of relations, especially when they
Externí odkaz:
http://arxiv.org/abs/2105.09045
Autor:
Lyu, Shengfei, Chen, Huanhuan
Relation classification aims to predict a relation between two entities in a sentence. The existing methods regard all relations as the candidate relations for the two entities in a sentence. These methods neglect the restrictions on candidate relati
Externí odkaz:
http://arxiv.org/abs/2105.08393
Neural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks. Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural netw
Externí odkaz:
http://arxiv.org/abs/2008.05282
Recent years, the approaches based on neural networks have shown remarkable potential for sentence modeling. There are two main neural network structures: recurrent neural network (RNN) and convolution neural network (CNN). RNN can capture long term
Externí odkaz:
http://arxiv.org/abs/2006.16174
Autor:
Lyu, Shengfei, Liu, Jiaqi
Convolutional neural network (CNN) and recurrent neural network (RNN) are two popular architectures used in text classification. Traditional methods to combine the strengths of the two networks rely on streamlining them or concatenating features extr
Externí odkaz:
http://arxiv.org/abs/2006.15795
The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently onl
Externí odkaz:
http://arxiv.org/abs/2006.15791
In the research area of time series classification, the ensemble shapelet transform algorithm is one of state-of-the-art algorithms for classification. However, its high time complexity is an issue to hinder its application since its base classifier
Externí odkaz:
http://arxiv.org/abs/1912.11982
In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the considerable amount of labeled r
Externí odkaz:
http://arxiv.org/abs/1906.01183