論文解説 Attention Is All You Need (Transformer) - ディープラーニングブログ
こんにちは Ryobot (りょぼっと) です． 本紙は RNN や CNN を使わず Attention のみ使用したニューラル機械翻訳 Transformer を提案している． わずかな訓練で圧倒的な State-of-the-Art を達成し，華麗にタイトル回収した． また注意を非常にシンプルな数式に一般化したうえで，加法注意・内積注意・ソースターゲット注意・自己注意に分類した．このうち自己注意はかなり汎用的かつ強力な手法であり他のあらゆるニューラルネットに転用できる． WMT'14 の BLEU スコアは英仏: 41.0, 英独: 28.4 で第 1 位 Attention Is All You Need [Łukasz Kaiser et al., arXiv, 2017/06] Transformer: A Novel Neural Network Architecture f
My Neural Network isn't working! What should I do?
Created on Aug. 19, 2017, 5:56 p.m. So you're developing the next great breakthrough in deep learning but you've hit an unfortunate setback: your neural network isn't working and you have no idea what to do. You go to your boss/supervisor but they don't know either - they are just as new to all of this as you - so what now? Well luckily for you I'm here with a list of all the things you've probabl
The Unreasonable Effectiveness of Recurrent Neural Networks
May 21, 2015 There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simpl
Image Classifier Demo
Upload your images to have them classified by a machine! Upload multiple images using the button below or dropping them on this page. The predicted objects out of 1,000 categories will be refreshed automatically. Images are resized such that the smallest dimension becomes 256, then the center 256x256 crop is used. More about this NYU demo can be found here. The model in the demo is early work towa
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画像認識に関する 機械学習技術 / kivantium さん - ニコナレ
