| Unsupervised anomaly detection with a GAN augmented autoencoder L Rafiee, T Fevens Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020 | 5 | 2020 |
| Minimizing client drift in federated learning via adaptive bias estimation F Varno, M Saghayi, L Rafiee, S Gupta, S Matwin, M Havaei arXiv preprint arXiv:2204.13170, 2022 | 3 | 2022 |
| Surface realization using pretrained language models F Farahnak, L Rafiee, L Kosseim, T Fevens Proceedings of the Third Workshop on Multilingual Surface Realisation, 57-63, 2020 | 3 | 2020 |
| The Concordia NLG Surface Realizer at SRST 2019 F Farahnak, L Rafiee, L Kosseim, T Fevens Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR …, 2019 | 2 | 2019 |
| AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection LR Sevyeri, T Fevens Image Analysis and Processing–ICIAP 2022: 21st International Conference …, 2022 | 1 | 2022 |
| AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation F Varno, M Saghayi, L Rafiee Sevyeri, S Gupta, S Matwin, M Havaei Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel …, 2022 | | 2022 |
| on the effectiveness of generative adversarial network on anomaly detection LR Sevyeri, T Fevens arXiv preprint arXiv:2112.15541, 2021 | | 2021 |
| Contrastive Generative Adversarial Network for Anomaly Detection LR Sevyeri, T Fevens | | 2021 |
| Learning from uncertain concepts via test time interventions I Sheth, AA Rahman, LR Sevyeri, M Havaei, SE Kahou Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022, 0 | | |
Thomas FevensProfessor, Department of Computer Science and Software Engineering, Concordia UniversityVerified email at cse.concordia.ca
