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study-note-20201009-20201022 study-note-20201009-20201022
一、学习内容 研读论文 CCG方向:《CCG Supertagging with a Recurrent Neural Network》 [^Question:the architecture of CCG Supertagging and
2020-10-22 WeiRuoHe
study-note-20201009-20201022 study-note-20201009-20201022
一、学习内容 研读尝试常识图谱相关的论文 《Automatic Extraction of Rules Governing Morphological Agreement》 《How to marry a star: probabilist
2020-10-22 WeiRuoHe
Code-allenlp Code-allenlp
yieldtext classification Defining input and outputAllenNLP one example:one instance:one or more Fields(each Field is a
2020-10-21
CCG-Parsing-LSTM CCG-Parsing-LSTM
document.querySelectorAll('.github-emoji') .forEach(el => { if (!el.dataset.src) { return
2020-10-20 WeiRuoHe
CCG-Supertagging-Faster-Parsing CCG-Supertagging-Faster-Parsing
document.querySelectorAll('.github-emoji') .forEach(el => { if (!el.dataset.src) { return
2020-10-20 WeiRuoHe
CCG-Supertagging-RNN CCG-Supertagging-RNN
Title《CCG Supertagging with a Recurrent Neural Network》 Abstract Recent work on supertagging using a feed-forward neural
CCG-Supertagging-LSTM CCG-Supertagging-LSTM
Title《Supertagging with LSTMs》 LSTM框架 [^LSTM中有三个门电路进行控制,门电路输出为1时门电路打开,反之关闭。]: 相比于Nerual Network,LSTM相当于外接四个电路进行输入,每次
CCG-Statistical-Parsing CCG-Statistical-Parsing
document.querySelectorAll('.github-emoji') .forEach(el => { if (!el.dataset.src) { return
2020-10-20 WeiRuoHe
SpecAugment-Data Augmentation-ASR SpecAugment-Data Augmentation-ASR
Title《SpecAugment: A Simple Data Augmentation Methodfor Automatic Speech Recognition》 Abstract We present SpecAugment, a
End2EndASR-SL2SSL End2EndASR-SL2SSL
Sourcehttps://arxiv.org/abs/1911.08460 title《END-TO-END ASR: FROM SUPERVISED TO SEMI-SUPERVISED LEARNING WITH MODERN ARC
Deep Belief Network-ASR.md Deep Belief Network-ASR.md
document.querySelectorAll('.github-emoji') .forEach(el => { if (!el.dataset.src) { return
Notes-LiHang Notes-LiHang
第六章 logistic regression model(logistic 回归模型) 拟牛顿法:https://blog.csdn.net/itplus/article/details/21896453 第九章 EM算法及其推广9.1
2020-10-11
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