Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Bingzhen, Wei"'
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 32:374-387
We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61f7c08f614f7300de648c91fa473ace
https://doi.org/10.1109/tkde.2018.2883613
https://doi.org/10.1109/tkde.2018.2883613
Publikováno v:
ACL (1)
Non-autoregressive translation models (NAT) have achieved impressive inference speedup. A potential issue of the existing NAT algorithms, however, is that the decoding is conducted in parallel, without directly considering previous context. In this p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b816a17a15a258acb04eadc091b90c61
https://doi.org/10.18653/v1/p19-1125
https://doi.org/10.18653/v1/p19-1125
Publikováno v:
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation. 40(2)
The current situation of the CO2 laser treatment apparatus in hospital was evaluated through actual investigation and testing data in this paper. The existing problems have been analyzed and the solutions are proposed, which wil play a role in mainte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::d08439a1fe96c2bb114fb23dccaa3705
https://pubmed.ncbi.nlm.nih.gov/29763232
https://pubmed.ncbi.nlm.nih.gov/29763232
ive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated summaries are often inconsistent with the source content in semantics. In such
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c011de13c3b498c55080e2d0539885b
http://arxiv.org/abs/1805.04033
http://arxiv.org/abs/1805.04033
Publikováno v:
Natural Language Processing and Chinese Computing ISBN: 9783319994949
NLPCC (1)
NLPCC (1)
Dependency parsing is an important NLP task. A popular approach for dependency parsing is structured perceptron. Still, graph-based dependency parsing has the time complexity of \(O(n^3)\), and it suffers from slow training. To deal with this problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f01ac0aaaf78b981ae9397843dd5f8a
https://doi.org/10.1007/978-3-319-99495-6_22
https://doi.org/10.1007/978-3-319-99495-6_22
Publikováno v:
Natural Language Processing and Chinese Computing ISBN: 9783319736174
NLPCC
NLPCC
Numerous machine learning tasks achieved substantial advances with the help of large-scale supervised learning corpora over past decade. However, there’s no large-scale question-answer corpora available for Chinese question answering over knowledge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0c82dc5bbbf95d5620dcd5372809d42
https://doi.org/10.1007/978-3-319-73618-1_7
https://doi.org/10.1007/978-3-319-73618-1_7
Publikováno v:
Natural Language Processing and Chinese Computing ISBN: 9783319736174
NLPCC
NLPCC
Recent studies have shown effectiveness in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resource datasets because of insufficient training data. We propose a transf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c28fd4e7bfbfdabfb278ab89d4c8e111
https://doi.org/10.1007/978-3-319-73618-1_62
https://doi.org/10.1007/978-3-319-73618-1_62
Publikováno v:
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation. 39(6)
Grounding impedance measurement is a traditional proficiency testing programs, 2014 proficiency testing program on the basis of original ability to verify, combined with actual detection need, innovation introduced two verification point of the power
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::1e3ab4ab19477ff42f9777a301e0e054
https://pubmed.ncbi.nlm.nih.gov/27066691
https://pubmed.ncbi.nlm.nih.gov/27066691