Deep Learning 第一战：
Code:
学习到的稀疏参数W1：
参考资料：
- UFLDL教程 稀疏自编码器
Autoencoders相关文章阅读:
- [3] Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets
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[4] Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 2006.
- If you want to play with the code, you can also find it at [5].
- [6] Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. Greedy Layer-Wise Training of Deep Networks. NIPS 2006
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[7] Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol. Extracting and Composing Robust Features with Denoising Autoencoders. ICML 2008.
- (They have a nice model, but then backwards rationalize it into a probabilistic model. Ignore the backwards rationalized probabilistic model [Section 4].)
【4】Reducing the dimensionality of data with neural networks，Hinton用的是RBM来pre-training参数
【5】
【6】Greedy Layer-Wise Training of Deep Networks中，Bengio例证了 RBM可以用autoencoder来替换，能得到相当的performance；探索了DBN的训练、对连续数值输入的适用问题、Dealing with uncooperative input distributions等。
【7】Extracting and Composing Robust Features with Denoising Autoencoders 处理带噪声/遮挡的图像