| Time series classification from scratch with deep neural networks: A strong baseline Z Wang, W Yan, T Oates 2017 International joint conference on neural networks (IJCNN), 1578-1585, 2017 | 507 | 2017 |
| Efficient progressive sampling F Provost, D Jensen, T Oates Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999 | 430 | 1999 |
| Clustering time series with hidden markov models and dynamic time warping T Oates, L Firoiu, PR Cohen Proceedings of the IJCAI-99 workshop on neural, symbolic and reinforcement …, 1999 | 247 | 1999 |
| The e! ects of training set size on decision tree complexity T Oates, D Jensen Proc. 14th Int. Conf. on Machine Learning, 1997 | 213 | 1997 |
| Detecting spam blogs: A machine learning approach P Kolari, A Java, T Finin, T Oates, A Joshi Proceedings of the national conference on artificial intelligence 21 (2), 1351, 2006 | 204 | 2006 |
| Modeling the spread of influence on the blogosphere A Java, P Kolari, T Finin, T Oates UMBC TR-CS-06-03, 2006 | 203 | 2006 |
| Encoding time series as images for visual inspection and classification using tiled convolutional neural networks Z Wang, T Oates Workshops at the twenty-ninth AAAI conference on artificial intelligence 1, 2015 | 171 | 2015 |
| Cooperative information-gathering: a distributed problem-solving approach T Oates, MVN Prasad, VR Lesser IEE Proceedings-Software 144 (1), 72-88, 1997 | 165 | 1997 |
| Imaging time-series to improve classification and imputation Z Wang, T Oates arXiv preprint arXiv:1506.00327, 2015 | 154 | 2015 |
| Identifying distinctive subsequences in multivariate time series by clustering T Oates Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999 | 129 | 1999 |
| Searching for structure in multiple streams of data T Oates, PR Cohen ICML 96, 346-354, 1996 | 125 | 1996 |
| A method for clustering the experiences of a mobile robot that accords with human judgments T Oates, MD Schmill, PR Cohen AAAI/IAAI, 846-851, 2000 | 113 | 2000 |
| Large Datasets Lead to Overly Complex Models: An Explanation and a Solution. T Oates, DD Jensen KDD, 294-298, 1998 | 101 | 1998 |
| PERUSE: An unsupervised algorithm for finding recurring patterns in time series T Oates 2002 IEEE International Conference on Data Mining, 2002. Proceedings., 330-337, 2002 | 98 | 2002 |
| Neo: Learning conceptual knowledge by sensorimotor interaction with an environment PR Cohen, MS Atkin, T Oates, CR Beal Proceedings of the first international conference on Autonomous agents, 170-177, 1997 | 94 | 1997 |
| A review of recent research in metareasoning and metalearning ML Anderson, T Oates AI Magazine 28 (1), 12-12, 2007 | 92 | 2007 |
| Hierarchical bayesian models for latent attribute detection in social media D Rao, M Paul, C Fink, D Yarowsky, T Oates, G Coppersmith Proceedings of the International AAAI Conference on Web and Social Media 5 (1), 2011 | 89 | 2011 |
| Visualizing variable-length time series motifs Y Li, J Lin, T Oates Proceedings of the 2012 SIAM international conference on data mining, 895-906, 2012 | 86 | 2012 |
| Using dynamic time warping to bootstrap HMM-based clustering of time series T Oates, L Firoiu, PR Cohen Sequence Learning, 35-52, 2000 | 86 | 2000 |
| The metacognitive loop I: Enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance ML Anderson, T Oates, W Chong, D Perlis Journal of Experimental and Theoretical Artificial Intelligence 18 (3), 387-411, 2006 | 85 | 2006 |
Paul R. CohenSchool of Computing and Information University of PittsburghVerified email at pitt.edu
