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Why Does Unsupervised Pre-training Help Deep Learning?
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The article was published on 2013-01-01 and is currently open access. It has received 560 citations till now. The article focuses on the topics: Deep learning.read more
Citations
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Proceedings ArticleDOI
Neural Collaborative Filtering
TL;DR: This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
Journal ArticleDOI
Deep convolutional neural networks for image classification: A comprehensive review
Waseem Rawat,Zenghui Wang +1 more
TL;DR: This review, which focuses on the application of CNNs to image classification tasks, covers their development, from their predecessors up to recent state-of-the-art deep learning systems.
BookDOI
Statistical Learning with Sparsity: The Lasso and Generalizations
TL;DR: Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data and extract useful and reproducible patterns from big datasets.
Proceedings Article
Recurrent convolutional neural networks for text classification
TL;DR: A recurrent convolutional neural network is introduced for text classification without human-designed features to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks.
Book ChapterDOI
Stacked convolutional auto-encoders for hierarchical feature extraction
TL;DR: A novel convolutional auto-encoder (CAE) for unsupervised feature learning that initializing a CNN with filters of a trained CAE stack yields superior performance on a digit and an object recognition benchmark.
References
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Journal Article
Why Does Unsupervised Pre-training Help Deep Learning?
Dumitru Erhan,Yoshua Bengio,Aaron Courville,Pierre-Antoine Manzagol,Pascal Vincent,Samy Bengio +5 more
TL;DR: In this paper, the authors empirically show the influence of pre-training with respect to architecture depth, model capacity, and number of training examples, and they suggest that unsupervised pretraining guides the learning towards basins of attraction of minima that support better generalization.