Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visua
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
In this work we propose a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length. Moreover, residual networks seem to enable very deep networks by leveraging only the short paths during training. To support this observation, we rewrite residual networks as an explicit collection of paths. Unlike traditional models, paths through re
Multi-Dimensional Recurrent Neural Networks
Recurrent neural networks (RNNs) have proved effective at one dimensional sequence learning tasks, such as speech and online handwriting recognition. Some of the properties that make RNNs suitable for such tasks, for example robustness to input warping, and the ability to access contextual information, are also desirable in multidimensional domains. However, there has so far been no direct way of
LONG SHORT-TERM MEMORY
LONG SHORT-TERM MEMORY Neural Computation 9(8):1735{1780, 1997 Sepp Hochreiter Fakultat fur Informatik Technische Universitat Munchen 80290 Munchen, Germany hochreit@informatik.tu-muenchen.de http://www7.informatik.tu-muenchen.de/~hochreit Jurgen Schmidhuber IDSIA Corso Elvezia 36 6900 Lugano, Switzerland juergen@idsia.ch http://www.idsia.ch/~juergen Abstract Learning to store information over ex
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence and the whole history of its preceding words in the sentence. Different from conventional topic models that largely ignore the sequential order of words or their topic coherence, SLRTM gives full characterization to them b
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A Novel Connectionist System for Unconstrained Handwriting Recognition - tpami_2009.pdf
CTC for Offline Handwriting Recognition
https://fhtw.byu.edu/static/conf/2016/nina-classification-fhtw2016.pdf
http://www.cs.toronto.edu/~graves/icml_2006.pdf
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks