Algorithmic Learning Theory: 22nd International Conference, by Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas

By Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (auth.), Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (eds.)

This publication constitutes the refereed court cases of the twenty second foreign convention on Algorithmic studying concept, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th overseas convention on Discovery technological know-how, DS 2011.
The 28 revised complete papers awarded including the abstracts of five invited talks have been conscientiously reviewed and chosen from various submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit difficulties, on-line studying, kernel and margin-based tools, clever brokers and different studying models.

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Additional resources for Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings

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Poly-logarithmic independence fools bounded-depth boolean circuits. : Quickly generating representative samples from an rbm-derived process. : Signature verification using a siamese time delay neural network. In: Advances in Pattern Recognition Systems using Neural Network Technologies, pp. 669–687. : Learning many related tasks at the same time with backpropagation. , Leen, T. ) Advances in Neural Information Processing Systems (NIPS 1994), vol. 7, pp. 657–664. : Learning a similarity metric discriminatively, with application to face verification.

Artificial Intelligence 40, 185–234 (1989) 34 Y. Bengio and O. : Products of experts. In: Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN), vol. 1, pp. 1–6. : Reducing the dimensionality of data with neural networks. : Autoencoders, minimum description length, and helmholtz free energy. , Alspector, J. ) Advances in Neural Information Processing Systems (NIPS 1993), vol. 6, pp. 3–10. : Boltzmann machines: Constraint satisfaction networks that learn. Technical Report TR-CMU-CS-84-119, Carnegie-Mellon University, Dept.

The idea of Denoising Auto-Encoders (DAE) is simple: feed the encoder/decoder system with a stochastically corrupted input, but ask it to reconstruct the clean input (as one would typically do to train any denoising system). 30 Y. Bengio and O. , 2010). , 2008; Vincent, 2011), and in particular through the Score Matching proxy for log-likelihood (Vincent, 2011): the denoising error corresponds to a form of regularized score matching criterion (Kingma and LeCun, 2010). The link also sheds light on why a denoising auto-encoder captures the input distribution.

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