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Autoencoder
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21	Published as a conference paper at ICLRM ULTI - TASK S EQUENCE TO S EQUENCE L EARNING Minh-Thang Luong∗, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser Google Brain ,{qvl,ilyasu,vin Add to Reading List Source URL: arxiv.org Language: English - Date: 2016-03-01 20:44:46 Machine learning Computational linguistics Artificial neural networks Computational neuroscience Computer-assisted translation Feature learning Parsing Machine translation Neural machine translation Autoencoder Evaluation of machine translation Multi-task learning
22	REVIEW doi:nature14539 Deep learning Yann LeCun1,2, Yoshua Bengio3 & Geoffrey Hinton4,5 Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2015-08-10 12:54:32 Artificial neural networks Deep learning Convolutional neural network Recurrent neural network Backpropagation Autoencoder Feature learning Recursive neural network Long short-term memory Perceptron Feedforward neural network Artificial intelligence
23	A Hierarchical Neural Autoencoder for Paragraphs and Documents Jiwei Li, Minh-Thang Luong and Dan Jurafsky Computer Science Department, Stanford University, Stanford, CA 94305, USA jiweil, lmthang, Add to Reading List Source URL: web.stanford.edu Language: English - Date: 2015-08-25 00:02:48 Computational linguistics Natural language processing Artificial neural networks Computational neuroscience Speech recognition Deep learning Long short-term memory Automatic summarization Sepp Hochreiter Parsing Feature learning N-gram
24	Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder Soroush Vosoughi∗ Prashanth Vijayaraghavan∗ Add to Reading List Source URL: socialmachines.media.mit.edu Language: English - Date: 2016-05-29 14:44:25 Artificial neural networks Computational neuroscience Deep learning Recurrent neural network Convolutional neural network Long short-term memory Tweet Artificial intelligence Autoencoder Convolution SemEval
25	Learning a Deep Hybrid Model for Semi-Supervised Text Classification Alexander G. Ororbia II, C. Lee Giles, David Reitter College of Information Sciences and Technology The Pennsylvania State University, University Park, Add to Reading List Source URL: www.anthology.aclweb.org Language: English - Date: 2015-09-09 11:04:44 Machine learning Cognition Applied mathematics Cognitive science Deep learning Semi-supervised learning Supervised learning Feature learning Artificial neural network Support vector machine Deep belief network Autoencoder
26	arXiv:1312.3429v2 [cs.CV] 16 DecUnsupervised learning of depth and motion Kishore Konda Goethe University Frankfurt Germany Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-12-16 20:39:51 Vision Light Stereoscopy Perception 3D imaging Computational neuroscience Visual perception Deep learning Autoencoder Stereopsis Binocular disparity Stereophonic sound
27	arXiv:1402.3337v2 [stat.ML] 10 NovZero-bias autoencoders and the benefits of co-adapting features Roland Memisevic Add to Reading List Source URL: arxiv.org Language: English - Date: 2014-11-11 20:54:00 Artificial neural networks Machine learning Learning Artificial intelligence Unsupervised learning Autoencoder Computational neuroscience Computational statistics Feature learning Rectifier Restricted Boltzmann machine Supervised learning
28	Deep Sparse Rectifier Neural Networks Xavier Glorot DIRO, Universit´e de Montr´eal Montr´eal, QC, Canada Add to Reading List Source URL: jmlr.csail.mit.edu Language: English - Date: 2011-06-30 04:28:55 Artificial neural networks Machine learning Applied mathematics Artificial intelligence Rectifier Deep learning Feature learning Autoencoder Backpropagation Sigmoid function Activation function Artificial neuron
29	arXiv:1110.0107v2 [cs.CV] 5 AprLearning to relate images: Mapping units, complex cells and simultaneous eigenspaces Roland Memisevic University of Frankfurt Add to Reading List Source URL: arxiv.org Language: English - Date: 2012-04-08 20:12:35 Machine learning Learning Artificial intelligence Estimation theory Computational neuroscience Computational statistics Dimension reduction Feature learning Mixture model Linear regression Autoencoder Gibbs sampling
30	UNSUPERVISED NEURAL NETWORK BASED FEATURE EXTRACTION USING WEAK TOP-DOWN CONSTRAINTS Herman Kamper1,2 , Micha Elsner3 , Aren Jansen4 , Sharon Goldwater2 1 CSTR and 2 ILCC, School of Informatics, University of Edinburgh, Add to Reading List Source URL: www.cstr.ed.ac.uk Language: English - Date: 2015-09-29 11:06:25 Machine learning Artificial neural networks Artificial intelligence Learning Applied mathematics Computational neuroscience Cybernetics Computational statistics Autoencoder Speech recognition Backpropagation Supervised learning

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