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The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All)
James Bergstra, Aaron Courville, Yoshua Bengio
Sparse coding is a proven principle for learning compact representations of images. However, sparse coding by itself often leads to very redundant dictionaries. With images, this o…
Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured Embeddings
Antoine Bordes, Xavier Glorot, Jason Weston +1
Open-text (or open-domain) semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR). Unfortunately, lar…
Learning invariant features through local space contraction
Salah Rifai, Xavier Muller, Xavier Glorot +3
We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function,…
Adding noise to the input of a model trained with a regularized objective
Salah Rifai, Xavier Glorot, Yoshua Bengio +1
Regularization is a well studied problem in the context of neural networks. It is usually used to improve the generalization performance when the number of input samples is relativ…
Autotagging music with conditional restricted Boltzmann machines
Michael Mandel, Razvan Pascanu, Hugo Larochelle +1
This paper describes two applications of conditional restricted Boltzmann machines (CRBMs) to the task of autotagging music. The first consists of training a CRBM to predict tags t…
Adaptive Drift-Diffusion Process to Learn Time Intervals
Francois Rivest, Yoshua Bengio
Animals learn the timing between consecutive events very easily. Their precision is usually proportional to the interval to time (Weber's law for timing). Most current timing model…