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Paper Summary]Text Understanding from Scratch. Text Understanding from Scratch. Text understanding, like other NLP tasks, requires prior knowledge such as a pre-defined dictionary, many varies and exceptions of words and rules. Approaches based on these ac-hoc assumptions are more specialized to an unique language, or even an unique tasks, and consequently less robust. 26 English letters, 10 digits, new line and 33 other characters are in character set:. Visualization of first layer weights:. Intuitively...

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iphone3310 | iphone3310.wordpress.com Reviews

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Paper Summary]Text Understanding from Scratch. Text Understanding from Scratch. Text understanding, like other NLP tasks, requires prior knowledge such as a pre-defined dictionary, many varies and exceptions of words and rules. Approaches based on these ac-hoc assumptions are more specialized to an unique language, or even an unique tasks, and consequently less robust. 26 English letters, 10 digits, new line and 33 other characters are in character set:. Visualization of first layer weights:. Intuitively...

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iphone3310 | iphone3310.wordpress.com Reviews

https://iphone3310.wordpress.com

Paper Summary]Text Understanding from Scratch. Text Understanding from Scratch. Text understanding, like other NLP tasks, requires prior knowledge such as a pre-defined dictionary, many varies and exceptions of words and rules. Approaches based on these ac-hoc assumptions are more specialized to an unique language, or even an unique tasks, and consequently less robust. 26 English letters, 10 digits, new line and 33 other characters are in character set:. Visualization of first layer weights:. Intuitively...

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iphone3310.wordpress.com

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[Paper Summary]Text Understanding from Scratch – iphone3310

https://iphone3310.wordpress.com/2016/06/21/paper-summarytext-understanding-from-scratch

Paper Summary]Text Understanding from Scratch. Text Understanding from Scratch. Text understanding, like other NLP tasks, requires prior knowledge such as a pre-defined dictionary, many varies and exceptions of words and rules. Approaches based on these ac-hoc assumptions are more specialized to an unique language, or even an unique tasks, and consequently less robust. 26 English letters, 10 digits, new line and 33 other characters are in character set:. Visualization of first layer weights:. Paper Summa...

2

三月 2016 – iphone3310

https://iphone3310.wordpress.com/2016/03

Paper Summary]Nonlinear Dimensionality Reduction by Locally Linear Embedding. Nonlinear Dimensionality Reduction by Locally Linear Embedding, Sam T. Roweis(1) and Lawrence K. Saul(2). Consider the graph above, A shows data distributed as something like tape, if you go with l2 distance as we commonly used, those blue ones are really close to red ones, and even more close to red ones than some other red ones! How can we map graph B into graph? Locally Linear Embedding is used here and above graph shows pro...

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iphone3310 – 2 頁

https://iphone3310.wordpress.com/page/2

Paper Summary]A Bayesian Hierarchical Model for Learning Natural Scene Categories. A Bayesian Hierarchical Model for Learning Natural Scene Categories, Li Fei-Fei, Pietro Perona. This work extends BOW with Bayesian hierarchical models, an “advanced PLSA" approach:. Above chart briefly shows how the algorithm works, but to go deeper, we need to dig inside and answer:How do images “chosen" by a model? Consequently, we have to determine “a distribution of topics" from “many distributions of topi...This proc...

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iphone3310 – iphone3310

https://iphone3310.wordpress.com/author/iphone3310

Paper Summary]Text Understanding from Scratch. Text Understanding from Scratch. Text understanding, like other NLP tasks, requires prior knowledge such as a pre-defined dictionary, many varies and exceptions of words and rules. Approaches based on these ac-hoc assumptions are more specialized to an unique language, or even an unique tasks, and consequently less robust. 26 English letters, 10 digits, new line and 33 other characters are in character set:. Visualization of first layer weights:. Intuitively...

5

四月 2016 – iphone3310

https://iphone3310.wordpress.com/2016/04

Paper Summary]Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. Deep Compression approach combined 3 methods, network pruning, weight sharing and huffman coding. As shown below:. Finally, huffman coding id preformed to non-uniformly distributed values saves 20% 30% of network storage:. When moving CNN to mobile, not only space and CPU concerns, en...

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iphone3310.wordpress.com

iphone3310

Paper Summary]Text Understanding from Scratch. Text Understanding from Scratch. Text understanding, like other NLP tasks, requires prior knowledge such as a pre-defined dictionary, many varies and exceptions of words and rules. Approaches based on these ac-hoc assumptions are more specialized to an unique language, or even an unique tasks, and consequently less robust. 26 English letters, 10 digits, new line and 33 other characters are in character set:. Visualization of first layer weights:. Intuitively...

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