Learning Deep Sigmoid Belief Networks with Data Augmentation Zhe Gan Ricardo Henao David Carlson Lawrence Carin

Source URL: jmlr.org

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,

Source URL: www.anthology.aclweb.org

• 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

Frankenstein: Learning Deep Face Representations using Small Data Guosheng Hu, Xiaojiang Peng, Yongxin Yang, Timothy Hospedales, Jakob Verbeek To cite this version:

Source URL: hal.inria.fr

• Artificial intelligence
• Learning
• Machine learning
• Computational neuroscience
• Applied mathematics
• Face recognition
• Artificial neural networks
• Computer vision
• Deep learning
• Convolutional neural network
• Eigenface
• Facial recognition system

Identification of Songbird Species in Field Recordings Hsiao-Yu Tung htung

Source URL: sfish0101.bitbucket.org

• Artificial intelligence
• Machine learning
• Learning
• Statistical classification
• Support vector machine
• K-nearest neighbors algorithm
• Classifier chains
• Multi-label classification
• Binary classification
• Boosting
• Deep learning
• Polynomial kernel

Learning Deep Representations of Fine-Grained Visual Descriptions Scott Reed1 , Zeynep Akata2 , Honglak Lee1 and Bernt Schiele2 arXiv:1605.05395v1 [cs.CV] 17 May

Source URL: arxiv.org

3D Object Classification using Shape Distributions and Deep Learning Melvin Low Stanford University Abstract

Source URL: cs231n.stanford.edu

Benchmark |Numenta Anomaly Benchmark| Numenta Anomaly Benchmark   The First Temporal Benchmark Designed to Evaluate

Source URL: numenta.com

• Deep learning
• Data mining
• Numenta
• Anomaly detection
• Jeff Hawkins
• Anomaly
• Benchmark
• On Intelligence

Multi-Agent Deep Reinforcement Learning Maxim Egorov Stanford University Abstract

Source URL: cs231n.stanford.edu

arXiv:1602.01783v1 [cs.LG] 4 FebAsynchronous Methods for Deep Reinforcement Learning Volodymyr Mnih1 Adri` a Puigdom`

Source URL: arxiv.org

Massively Parallel Methods for Deep Reinforcement Learning arXiv:1507.04296v2 [cs.LG] 16 Jul 2015 Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam,

Source URL: arxiv.org