| 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 | 2024 | 2017 |
| Imaging time-series to improve classification and imputation Z Wang, T Oates Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 789 | 2015 |
| 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, 2015 | 605 | 2015 |
| Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation Z Wang, J Yang AAAI 2018, 2018 | 150 | 2018 |
| Gated transformer networks for multivariate time series classification M Liu, S Ren, S Ma, J Jiao, Y Chen, Z Wang, W Song arXiv preprint arXiv:2103.14438, 2021 | 123 | 2021 |
| Continual learning in task-oriented dialogue systems A Madotto, Z Lin, Z Zhou, S Moon, P Crook, B Liu, Z Yu, E Cho, Z Wang arXiv preprint arXiv:2012.15504, 2020 | 93 | 2020 |
| Leveraging slot descriptions for zero-shot cross-domain dialogue state tracking Z Lin, B Liu, S Moon, P Crook, Z Zhou, Z Wang, Z Yu, A Madotto, E Cho, ... arXiv preprint arXiv:2105.04222, 2021 | 77 | 2021 |
| Adding chit-chat to enhance task-oriented dialogues K Sun, S Moon, P Crook, S Roller, B Silvert, B Liu, Z Wang, H Liu, E Cho, ... arXiv preprint arXiv:2010.12757, 2020 | 63 | 2020 |
| Spatially encoding temporal correlations to classify temporal data using convolutional neural networks Z Wang, T Oates arXiv preprint arXiv:1509.07481, 2015 | 63 | 2015 |
| Zero-shot dialogue state tracking via cross-task transfer Z Lin, B Liu, A Madotto, S Moon, P Crook, Z Zhou, Z Wang, Z Yu, E Cho, ... arXiv preprint arXiv:2109.04655, 2021 | 51 | 2021 |
| Automated cloud provisioning on aws using deep reinforcement learning Z Wang, C Gwon, T Oates, A Iezzi arXiv preprint arXiv:1709.04305, 2017 | 30 | 2017 |
| Imaging time-series to improve classification and imputation. arXiv 2015 Z Wang, T Oates arXiv preprint arXiv:1506.00327 1506, 0 | 29 | |
| Self-learning augmented reality for industrial operations B Singh, W Zhiguang, J Yang, S Murugappan, J Nichols US Patent App. 15/678,654, 2019 | 27 | 2019 |
| Representation learning with deconvolution for multivariate time series classification and visualization Z Wang, W Song, L Liu, F Zhang, J Xue, Y Ye, M Fan, M Xu arXiv preprint arXiv:1610.07258, 2016 | 17 | 2016 |
| Taylor genetic programming for symbolic regression B He, Q Lu, Q Yang, J Luo, Z Wang Proceedings of the genetic and evolutionary computation conference, 946-954, 2022 | 16 | 2022 |
| Information seeking in the spirit of learning: A dataset for conversational curiosity P Rodriguez, P Crook, S Moon, Z Wang arXiv preprint arXiv:2005.00172, 2020 | 14 | 2020 |
| Representation learning with deconvolution for multivariate time series classification and visualization W Song, L Liu, M Liu, W Wang, X Wang, Y Song Data Science: 6th International Conference of Pioneering Computer Scientists …, 2020 | 14 | 2020 |
| Empirical study of symbolic aggregate approximation for time series classification W Song, Z Wang, F Zhang, Y Ye, M Fan Intelligent Data Analysis 21 (1), 135-150, 2017 | 13 | 2017 |
| Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series L Liu, Z Wang AAAI 2018, 2018 | 11 | 2018 |
| Time series classification from scratch with deep neural networks: A strong baseline. arXiv 2016 Z Wang, W Yan, T Oates arXiv preprint arXiv:1611.06455, 0 | 11 | |
Tim OatesProfessor of Computer Science, University of Maryland Baltimore CountyBestätigte E-Mail-Adresse bei umbc.edu
