Get our free extension to see links to code for papers anywhere online!

 Add to Chrome

 Add to Firefox

CatalyzeX Code Finder - Browser extension linking code for ML papers across the web! | Product Hunt Embed
Locality Preserving Loss to Align Vector Spaces

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

  Access Model/Code and Paper
Using Neural Networks for Programming by Demonstration

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

  Access Model/Code and Paper
Universal Adversarial Perturbation for Text Classification

Oct 10, 2019
Hang Gao, Tim Oates

  Access Model/Code and Paper
Learning from Observations Using a Single Video Demonstration and Human Feedback

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

  Access Model/Code and Paper
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 

  Access Model/Code and Paper
Graph Node Embeddings using Domain-Aware Biased Random Walks

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

  Access Model/Code and Paper
Hybrid Mortality Prediction using Multiple Source Systems

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

  Access Model/Code and Paper
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning

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

  Access Model/Code and Paper
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

  Access Model/Code and Paper
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning

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

  Access Model/Code and Paper
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 

  Access Model/Code and Paper
Fashioning with Networks: Neural Style Transfer to Design Clothes

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

* ML4Fashion 2017 

  Access Model/Code and Paper
Identifying Spatial Relations in Images using Convolutional Neural Networks

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

  Access Model/Code and Paper
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

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

  Access Model/Code and Paper
Neuroevolution-Based Inverse Reinforcement Learning

Aug 09, 2016
Karan K. Budhraja, Tim Oates

* 12 pages, 15 figures 

  Access Model/Code and Paper
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 

  Access Model/Code and Paper
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 

  Access Model/Code and Paper
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] 

  Access Model/Code and Paper
Imaging Time-Series to Improve Classification and Imputation

Jun 01, 2015
Zhiguang Wang, Tim Oates

* Accepted by IJCAI-2015 ML track 

  Access Model/Code and Paper
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 

  Access Model/Code and Paper
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) 

  Access Model/Code and Paper