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Locality Preserving Loss to Align Vector Spaces

Apr 07, 2020
Ashwinkumar Ganesan, Frank Ferraro, Tim Oates


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Using Neural Networks for Programming by Demonstration

Oct 10, 2019
Karan K. Budhraja, Hang Gao, Tim Oates


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Universal Adversarial Perturbation for Text Classification

Oct 10, 2019
Hang Gao, Tim Oates


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Learning from Observations Using a Single Video Demonstration and Human Feedback

Sep 29, 2019
Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas Waytowich


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Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter

Sep 12, 2019
Chi Zhang, Bryan Wilkinson, Ashwinkumar Ganesan, Tim Oates

* DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security Workshop, December 3--4, 2018, San Juan, PR, USA 

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Graph Node Embeddings using Domain-Aware Biased Random Walks

Aug 08, 2019
Sourav Mukherjee, Tim Oates, Ryan Wright


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Hybrid Mortality Prediction using Multiple Source Systems

Apr 18, 2019
Isaac Mativo, Yelena Yesha, Michael Grasso, Tim Oates, Qian Zhu


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Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning

Mar 28, 2019
Ashwinkumar Ganesan, Pooja Parameshwarappa, Akshay Peshave, Zhiyuan Chen, Tim Oates


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On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning

Mar 25, 2019
Bharat Prakash, Mark Horton, Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin


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Automated Cloud Provisioning on AWS using Deep Reinforcement Learning

Sep 19, 2017
Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi


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Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

Aug 28, 2017
JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

* Old draft of AAAI paper, AAAI Spring Symposium Series. 2014 

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Fashioning with Networks: Neural Style Transfer to Design Clothes

Jul 31, 2017
Prutha Date, Ashwinkumar Ganesan, Tim Oates

* ML4Fashion 2017 

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Identifying Spatial Relations in Images using Convolutional Neural Networks

Jun 13, 2017
Mandar Haldekar, Ashwinkumar Ganesan, Tim Oates


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Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

Dec 14, 2016
Zhiguang Wang, Weizhong Yan, Tim Oates


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Neuroevolution-Based Inverse Reinforcement Learning

Aug 09, 2016
Karan K. Budhraja, Tim Oates

* 12 pages, 15 figures 

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Adaptive Normalized Risk-Averting Training For Deep Neural Networks

Jun 09, 2016
Zhiguang Wang, Tim Oates, James Lo

* AAAI 2016, 0.39%~0.4% ER on MNIST with single 32-32-256-10 ConvNets, code available at https://github.com/cauchyturing/ANRAE 

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Adopting Robustness and Optimality in Fitting and Learning

Dec 15, 2015
Zhiguang Wang, Tim Oates, James Lo

* This paper has been withdrawn by the authors due to some errors and confusions in terminology 

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Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks

Sep 24, 2015
Zhiguang Wang, Tim Oates

* Submit to JCSS. Preliminary versions are appeared in AAAI 2015 workshop and IJCAI 2016 [arXiv:1506.00327] 

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Imaging Time-Series to Improve Classification and Imputation

Jun 01, 2015
Zhiguang Wang, Tim Oates

* Accepted by IJCAI-2015 ML track 

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Detecting Epileptic Seizures from EEG Data using Neural Networks

Apr 21, 2015
Siddharth Pramod, Adam Page, Tinoosh Mohsenin, Tim Oates

* This paper has been withdrawn by the authors due to an error discovered in the experiments 

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The Thing That We Tried Didn't Work Very Well : Deictic Representation in Reinforcement Learning

Dec 12, 2012
Sarah Finney, Natalia Gardiol, Leslie Pack Kaelbling, Tim Oates

* Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002) 

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