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1.
Text Representation using Convolutional Networks
by Zhang, Xiang, Ph.D.  New York University. 2019: 164 pages; 10928487.
2.
Learning in chaotic recurrent neural networks
by Sussillo, David C., Ph.D.  Columbia University. 2009: 134 pages; 3346497.
3.
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.
4.
Learning from Temporally-Structured Human Activities Data
by Lipton, Zachary C., Ph.D.  University of California, San Diego. 2017: 274 pages; 10683703.
5.
Variable Memory Recurrent Neural Networks for Nano Sat Launch Vehicle attitude control
by Sclafani, Rodolfo J., M.S.  California State University, Long Beach. 2014: 79 pages; 1527749.
6.
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.
7.
DiffFrameNet: A Deep Learning Method for Intuitive Robot Navigation
by Cao, Bo, M.S.  University of Colorado at Boulder. 2018: 69 pages; 10809457.
8.
Attention to Deep Structure in Recurrent Neural Networks
by Sharpe, Spencer S., M.S.  University of Wyoming. 2017: 62 pages; 10619110.
9.
Electroencephalogram classification by forecasting with recurrent neural networks
by Forney, Elliott M., M.S.  Colorado State University. 2011: 79 pages; 1503664.
10.
Deep Networks for Forward Prediction and Planning
by Henaff, Mikael, Ph.D.  New York University. 2018: 164 pages; 10928805.
11.
Spacecraft Formation Control: Adaptive PID-Extended Memory Recurrent Neural Network Controller
by Gonzalez, Juan, M.S.  California State University, Long Beach. 2018: 76 pages; 10978237.
12.
Modeling the mirror system in action observation and execution
by Bonaiuto, James, Ph.D.  University of Southern California. 2010: 291 pages; 3418250.
13.
Deep Learning for Information Extraction
by Nguyen, Thien Huu, Ph.D.  New York University. 2018: 246 pages; 10260911.
15.
Automated Feature Engineering for Deep Neural Networks with Genetic Programming
by Heaton, Jeff, Ph.D.  Nova Southeastern University. 2017: 201 pages; 10259604.
16.
Towards Temporal Semantic Scene Understanding
by Ghafarianzadeh, Mahsa, Ph.D.  University of Colorado at Boulder. 2017: 152 pages; 10271366.
18.
Representation Learning on Sequential Medical Data
by Hyland, Stephanie L., Ph.D.  Weill Medical College of Cornell University. 2019: 245 pages; 13805668.
19.
Risk Factors for Recurrent Major Depressive Disorder in a Nationally Representative Sample
by DeFeo, Graig C., M.S.P.H.  University of South Florida. 2014: 84 pages; 1569946.
20.
Learning to Learn with Gradients
by Finn, Chelsea B., Ph.D.  University of California, Berkeley. 2018: 199 pages; 10930398.
21.
Predicting Risk Of Recurrent Stroke Based On Changes In Risk Factors: The VISP Study
by Campbell, Stephen Michael, Ph.D.  The University of North Carolina at Chapel Hill. 2011: 280 pages; 3509245.
22.
Automatic Snooker-Playing Robot with Speech Recognition Using Deep Learning
by Bhagat, Kunj H., M.S.  California State University, Long Beach. 2018: 61 pages; 10977867.
23.
Machine Learning Methods for 3D Object Classification and Segmentation
by Le, Truc Duc, Ph.D.  University of Missouri - Columbia. 2018: 158 pages; 13877153.
24.
25.
A study of cell-based genetic algorithms with applications to neural networks
by Dinh, Hoa, M.S.  California State University, Long Beach. 2013: 62 pages; 1527480.
26.
Decoding the rhythms of avian auditory LFP
by Schachter, Mike J., Ph.D.  University of California, Berkeley. 2016: 53 pages; 10192590.
28.
The synaptic dynamics of working memory in a genetic model of psychosis
by Arguello, Paul Alexander, Ph.D.  Columbia University. 2010: 109 pages; 3400626.
30.
Creative Learning for Intelligent Robots
by Liao, Xiaoqun, Ph.D.  University of Cincinnati. 2006: 295 pages; 10857161.
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