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1.
Spiking Neural Networks and Sparse Deep Learning
by Tavanaei, Amirhossein, Ph.D.  University of Louisiana at Lafayette. 2018: 176 pages; 10807940.
2.
Deep Learning for Information Extraction
by Nguyen, Thien Huu, Ph.D.  New York University. 2018: 246 pages; 10260911.
3.
Self -taught learning
by Raina, Rajat, Ph.D.  Stanford University. 2009: 155 pages; 3382948.
4.
Geometric representations and deep Gaussian conditional random field networks for computer vision
by Vemulapalli, Raviteja, Ph.D.  University of Maryland, College Park. 2016: 185 pages; 10192530.
5.
Learning Neural Representations that Support Efficient Reinforcement Learning
by Stachenfeld, Kimberly, Ph.D.  Princeton University. 2018: 155 pages; 10824319.
6.
Integrating Multiple Modalities into Deep Learning Networks
by McNeil, Patrick N., Ph.D.  Nova Southeastern University. 2017: 296 pages; 10283417.
7.
Improving Knowledge Graph Quality with Network Representation Learning
by Shi, Baoxu, Ph.D.  University of Notre Dame. 2018: 108 pages; 13836462.
8.
Learning to Learn with Gradients
by Finn, Chelsea B., Ph.D.  University of California, Berkeley. 2018: 199 pages; 10930398.
9.
Semi-supervised learning for connectionist networks
by Robare, Rebecca J., Ph.D.  City University of New York. 2010: 117 pages; 3426834.
10.
Learning to Manipulate Novel Objects for Assistive Robots
by Sung, Jaeyong, Ph.D.  Cornell University. 2017: 190 pages; 10258261.
11.
Unsupervised Learning under Uncertainty
by Mathieu, MichaĆ«l, Ph.D.  New York University. 2017: 159 pages; 10261120.
12.
Towards Fast and Efficient Representation Learning
by Li, Hao, Ph.D.  University of Maryland, College Park. 2018: 147 pages; 10845690.
13.
Text Representation using Convolutional Networks
by Zhang, Xiang, Ph.D.  New York University. 2019: 164 pages; 10928487.
14.
Leveraging Deep Neural Networks to Study Human Cognition
by Peterson, Joshua C., Ph.D.  University of California, Berkeley. 2018: 129 pages; 10930700.
15.
Comparison and Fine-Grained Analysis of Sequence Encoders for Natural Language Processing
by Keller, Thomas Anderson, M.S.  University of California, San Diego. 2017: 87 pages; 10599339.
16.
Visual Representations for Fine-grained Categorization
by Zhang, Ning, Ph.D.  University of California, Berkeley. 2015: 83 pages; 10086169.
17.
18.
Learning temporal representations in cortical networks through reward dependent expression of synaptic plasticity
by Gavornik, Jeffrey Peter, Ph.D.  The University of Texas at Austin. 2009: 121 pages; 3360319.
20.
Deep Learning for Attribute Inference, Parsing, and Recognition of Face
by Luo, Ping, Ph.D.  The Chinese University of Hong Kong (Hong Kong). 2014: 122 pages; 3691916.
21.
Human Activity Analysis using Multi-modalities and Deep Learning
by Zhang, Chenyang, Ph.D.  The City College of New York. 2016: 115 pages; 10159927.
22.
Belief based reinforcement learning for data fusion
by Lollett, Carlos, Ph.D.  State University of New York at Buffalo. 2009: 184 pages; 3342148.
23.
Application of temporal difference learning to the game of Snake
by Lockhart, Christopher, M.Eng.  University of Louisville. 2010: 96 pages; 1485160.
24.
Adaptive control and learning using multiple models
by Wang, Yu, Ph.D.  Yale University. 2017: 229 pages; 10783473.
25.
Representation Learning on Sequential Medical Data
by Hyland, Stephanie L., Ph.D.  Weill Medical College of Cornell University. 2019: 245 pages; 13805668.
26.
Feature Learning as a Tool to Identify Existence of Multiple Biological Patterns
by Patsekin, Aleksandr, M.S.  Purdue University. 2018: 69 pages; 10807747.
27.
Deep Networks for Forward Prediction and Planning
by Henaff, Mikael, Ph.D.  New York University. 2018: 164 pages; 10928805.
28.
Modeling the mirror system in action observation and execution
by Bonaiuto, James, Ph.D.  University of Southern California. 2010: 291 pages; 3418250.
29.
Attention to Deep Structure in Recurrent Neural Networks
by Sharpe, Spencer S., M.S.  University of Wyoming. 2017: 62 pages; 10619110.
30.
Latent Variable Modeling for Networks and Text: Algorithms, Models and Evaluation Techniques
by Foulds, James Richard, Ph.D.  University of California, Irvine. 2014: 287 pages; 3631094.
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