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## 一言でいうと
CNNを使って語順を考慮したテキスト分類を行う話。たいていのCNNの手法では入力としてword embeddingを入力するが、この研究では高次元のone-hotベクトルをそのまま入力して、小さなテキスト領域のembeddingを学習する。評判分析(IMDB含む)とトピック分類に関する3つのデータセットでSOTAな手法と比較した結果、提案手法の有効性を示せた。
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1. 阅读文献 “Imagenet classification with deep convolutional neural networks”
2. 挑一个Caffe里的一个例子，详细理解Solver文件里每一个超参数的意义。
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This suggestion was moved from Neataptic.
https://github.com/wagenaartje/neataptic/issues/54
> In order to amplify the results of neural networks on larger datasets (i.e. MNIST), we need to implem…
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**Main problem:**
Law enforcement organisations can take proactive measures through early identification of predatory activities in cyberspace thanks to automatic detection of predatory communication…
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Could anyone advise on how to derive _matrices G and B_ from matrix _A_ using [Fast Algorithms for Convolutional Neural Networks](https://arxiv.org/pdf/1509.09308.pdf#page=2) ?
:red_circle: **Project Title** : Employee Turnover
:red_circle: **Aim** : Predict Employee Turnover with scikitlearn
:red_circle: **Dat…
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http://www.cs.cmu.edu/~./callan/Papers/wsdm18-zhuyun-dai.pdf
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**Info:**
- OS: Windows 10
- Logseq ver: 8.18
- Plugin ver: 1.10.0
- Theme name:
- [ ] ⚠ Do i have issue with DISABLED my `custom.css`?
**Description:**
Due to alignment of page property va…