| Marine mammal species classification using convolutional neural networks and a novel acoustic representation M Thomas, B Martin, K Kowarski, B Gaudet, S Matwin Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 53 | 2020 |
| ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices A Theissler, M Thomas, M Burch, F Gerschner Knowledge-Based Systems 247, 108651, 2022 | 28 | 2022 |
| Inverse problem for time-series valued computer model via scalarization P Ranjan, M Thomas, H Teismann, S Mukhoti arXiv preprint arXiv:1605.09503, 2016 | 16 | 2016 |
| An end-to-end approach for true detection of low frequency marine mammal vocalizations M Thomas, B Martin, K Kowarski, B Gaudet, S Matwin The Journal of the Acoustical Society of America 146 (4_Supplement), 2959-2959, 2019 | 6 | 2019 |
| Towards a Novel Data Representation for Classifying Acoustic Signals M Thomas Advances in Artificial Intelligence: 32nd Canadian Conference on Artificial …, 2019 | 1 | 2019 |
| Interpreting the latent representations of a convolutional neural network trained on spectrograms M Thomas, B Martin, K Kowarski, B Gaudet, S Matwin The Journal of the Acoustical Society of America 146 (4_Supplement), 2958-2959, 2019 | | 2019 |
| Leveraging Unlabelled Data through Semi-supervised Learning to Improve the Performance of a Marine Mammal Classification System M Thomas, B Martin, S Matwin | | |
| Detecting Endangered Baleen Whales within Acoustic Recordings using R-CNNs M Thomas, B Martin, K Kowarski, B Gaudet, S Matwin | | |
Stan MatwinDalhousie University, Halifax, Canada, Institute of Computer Science, Polish Academy of SciencesVerified email at cs.dal.ca
