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Hsuan-ting Chen AMMAI Note

Hsuan-ting Chen AMMAI Note. Ammai] Sequence to Sequence – Video to Text. Sequence to Sequence – Video to Text. Subhashini Venugopalan, et al. This paper performs a approach for video captioning in a simple LSTM model. The LSTM model can fit with different input and output size. Its structure is very simple, but it performs well. LSTM is a famous method for sequence to sequence task such as speech recognition, and translation. Their architecture, S2Vt is as follows:. This paper perform neural network arch...

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Hsuan-ting Chen AMMAI Note | htcammai.blogspot.com Reviews

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Hsuan-ting Chen AMMAI Note. Ammai] Sequence to Sequence – Video to Text. Sequence to Sequence – Video to Text. Subhashini Venugopalan, et al. This paper performs a approach for video captioning in a simple LSTM model. The LSTM model can fit with different input and output size. Its structure is very simple, but it performs well. LSTM is a famous method for sequence to sequence task such as speech recognition, and translation. Their architecture, S2Vt is as follows:. This paper perform neural network arch...

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Hsuan-ting Chen AMMAI Note | htcammai.blogspot.com Reviews

https://htcammai.blogspot.com

Hsuan-ting Chen AMMAI Note. Ammai] Sequence to Sequence – Video to Text. Sequence to Sequence – Video to Text. Subhashini Venugopalan, et al. This paper performs a approach for video captioning in a simple LSTM model. The LSTM model can fit with different input and output size. Its structure is very simple, but it performs well. LSTM is a famous method for sequence to sequence task such as speech recognition, and translation. Their architecture, S2Vt is as follows:. This paper perform neural network arch...

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Hsuan-ting Chen AMMAI Note: 三月 2016

http://htcammai.blogspot.com/2016_03_01_archive.html

Hsuan-ting Chen AMMAI Note. Ammai2016] Nonlinear Dimensionality Reduction by Locally Linear Embedding. Date: March 24th, 2016. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Sam T Roweis and Lawrence K. Saul. 1 Introduce LLE(locally linear embedding), which is an unsupervised and neighborhood-preserving embeddings. 1 The method is straightforward, the only free parameter is the number of neighbors, K. 2 LLE does not have to be rerun when more dimensions are added to it. There are three c...

2

Hsuan-ting Chen AMMAI Note: [ammai] DeepFace: Closing the Gap to Human-Level Performance in Face Verification

http://htcammai.blogspot.com/2016/05/ammai-deepface-closing-gap-to-human.html

Hsuan-ting Chen AMMAI Note. Ammai] DeepFace: Closing the Gap to Human-Level Performance in Face Verification. DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Taigman, et al. This paper shows some new methds for face classifier. 1 3D model-based alignment. 2 large capacity feedforward model. This paper introduces DeepFace which is close to human level accuracy on face recognition. In modern face recognition, there is four stages: detect= align= represent= classify. Then there ar...

3

Hsuan-ting Chen AMMAI Note: 四月 2016

http://htcammai.blogspot.com/2016_04_01_archive.html

Hsuan-ting Chen AMMAI Note. Ammai] DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING. Date: April 21st, 2016. DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING. Song Han, Huizi Mao. William J. Dally. Compress deep neural networks to fit in embedded systems without loss precision. They use three methods to compress deep neural networks:. 1 Pruning the weights. 3 Huffman encoding the weights. The r...

4

Hsuan-ting Chen AMMAI Note: 六月 2016

http://htcammai.blogspot.com/2016_06_01_archive.html

Hsuan-ting Chen AMMAI Note. Ammai] Sequence to Sequence – Video to Text. Sequence to Sequence – Video to Text. Subhashini Venugopalan, et al. This paper performs a approach for video captioning in a simple LSTM model. The LSTM model can fit with different input and output size. Its structure is very simple, but it performs well. LSTM is a famous method for sequence to sequence task such as speech recognition, and translation. Their architecture, S2Vt is as follows:. 訂閱：文章 (Atom).

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Hsuan-ting Chen AMMAI Note: [ammai] DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING

http://htcammai.blogspot.com/2016/04/ammai-deep-compression-compressing-deep.html

Hsuan-ting Chen AMMAI Note. Ammai] DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING. Date: April 21st, 2016. DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING. Song Han, Huizi Mao. William J. Dally. Compress deep neural networks to fit in embedded systems without loss precision. They use three methods to compress deep neural networks:. 1 Pruning the weights. 3 Huffman encoding the weights. The r...

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Hsuan-ting Chen AMMAI Note

Hsuan-ting Chen AMMAI Note. Ammai] Sequence to Sequence – Video to Text. Sequence to Sequence – Video to Text. Subhashini Venugopalan, et al. This paper performs a approach for video captioning in a simple LSTM model. The LSTM model can fit with different input and output size. Its structure is very simple, but it performs well. LSTM is a famous method for sequence to sequence task such as speech recognition, and translation. Their architecture, S2Vt is as follows:. This paper perform neural network arch...

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