Syntax-Directed Variational Autoencoder for Molecule Generation Hanjun Dai1* , Yingtao Tian2* , Bo Dai1 , Steven Skiena2 , Le Song1 1 College of Computing, Georgia Institute of Technology 2

Source URL: www.quantum-machine.org

Semi-Supervised Recursive Autoencoder Si Chen and Yufei Wang Department of Electrical and Computer Engineering University of California San Diego {sic046, yuw176}@ucsd.edu

Source URL: acsweb.ucsd.edu

An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P1 ∗ , Stanislas Lauly2 ∗ , Hugo Larochelle2 , Mitesh M Khapra3 , Balaraman Ravindran1 , Vikas Raykar3 , Amrita Saha3 1

Source URL: info.usherbrooke.ca

On Nonparametric Guidance for Learning Autoencoder Representations Jasper Snoek University of Toronto

Source URL: info.usherbrooke.ca

arXiv:1610.00291v1 [cs.CV] 2 OctDeep Feature Consistent Variational Autoencoder Xianxu Hou University of Nottingham, Ningbo China

Source URL: arxiv.org

CS294A Lecture notes Andrew Ng Sparse autoencoder 1

Source URL: www.stanford.edu

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,

Source URL: aclweb.org

NCSU_SAS_SAM: Deep Encoding and Reconstruction for Normalization of Noisy Text Samuel P. Leeman-Munk James C. Lester Center for Educational Informatics North Carolina State University

Source URL: noisy-text.github.io

• Artificial neural networks
• Cybernetics
• Applied mathematics
• Artificial intelligence
• Deep learning
• Convolutional neural network
• Feature learning
• Backpropagation
• Autoencoder
• Artificial neuron
• Feedforward neural network

Low-Complexity Coding and Decoding Sepp Hochreiter and Jurgen Schmidhuber Technische Universitat Munchen, 80290 Munchen, Germany and IDSIA, Corso Elvezia 36, CH-6900-Lugano, Switzerland Abstract. We present a novel

Source URL: www.bioinf.jku.at

• Artificial neural networks
• Applied mathematics
• Machine learning
• Statistics
• Factorial code
• Sepp Hochreiter
• Backpropagation
• Competitive learning
• Self-organizing map
• Autoencoder

ELU-Networks: Fast and Accurate CNN Learning on ImageNet Martin Heusel, Djork-Arné Clevert, Günter Klambauer, Andreas Mayr, Karin Schwarzbauer, Thomas Unterthiner, and Sepp Hochreiter Abstract:

Source URL: www.bioinf.jku.at

• Artificial neural networks
• Machine learning
• Artificial intelligence
• Cybernetics
• Sepp Hochreiter
• Deep learning
• Rectifier
• Activation function
• MNIST database
• Backpropagation
• Autoencoder
• Boltzmann machine