Results 21 to 30 of about 7,691,602 (298)
Multi-Level Matching Networks for Text Matching [PDF]
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019 Text matching aims to establish the matching relationship between two texts. It is an important operation in some information retrieval related tasks such as question duplicate detection, question answering, and dialog systems. Bidirectional long short term memory (BiLSTM) coupled with attention mechanism has achieved state-of-the-art performance in ...
Chunlin Xu +3 more
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Power Amplifiers With Frequency-Selective Matching Networks
IEEE transactions on microwave theory and techniques, 2021 In this article, we demonstrate a method for codesign of filtering matching networks for power amplifiers (PAs) with the desired frequency response, improved efficiency, and reduced footprint. The microwave transistor operates with high efficiency with a
J. Estrada +4 more
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Multilevel Graph Matching Networks for Deep Graph Similarity Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 While the celebrated graph neural networks (GNNs) yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning.
Xiang Ling +7 more
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Refinement of matching costs for stereo disparities using recurrent neural networks
EURASIP Journal on Image and Video Processing, 2021 Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume
Alper Emlek, Murat Peker
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Subgraph Matching on Multiplex Networks
IEEE Transactions on Network Science and Engineering, 2021 An active area of research in computational science is the design of algorithms for solving the subgraph matching problem to find copies of a given template graph in a larger world graph. Prior works have largely addressed single-channel networks using a variety of approaches.
Jacob D. Moorman +4 more
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Pyramid Stereo Matching Network [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in illposed regions. To tackle
Jia-Ren Chang, Yong-Sheng Chen
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Gated Self-Matching Networks for Reading Comprehension and Question Answering
Annual Meeting of the Association for Computational Linguistics, 2017 In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage.
Wenhui Wang +4 more
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Match memory recurrent networks [PDF]
2016 International Joint Conference on Neural Networks (IJCNN), 2016 Imbuing neural networks with memory and attention mechanisms allows for better generalisation with fewer data samples. By focusing only on the relevant parts of data, which is encoded in an internal “memory” format, the network is able to infer better and more reliable patterns.
Spyridon Samothrakis +3 more
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Combining Fact Extraction and Verification with Neural Semantic Matching Networks [PDF]
AAAI Conference on Artificial Intelligence, 2018 The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences
Yixin Nie, Haonan Chen, Mohit Bansal
semanticscholar +1 more source
IEEE Access, 2020 Identifying corresponding objects from geospatial databases at different levels of detail is crucial, especially in multi-scale road network matching, which is the prerequisite of data conflation, updating and quality assessment.
Qingsheng Guo +3 more
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