| Meta-learning for semi-supervised few-shot classification M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ... arXiv preprint arXiv:1803.00676, 2018 | 1722 | 2018 |
| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... arXiv preprint arXiv:1903.03096, 2019 | 761 | 2019 |
| Few-shot learning through an information retrieval lens E Triantafillou, R Zemel, R Urtasun Advances in neural information processing systems 30, 2017 | 286 | 2017 |
| Towards unbounded machine unlearning M Kurmanji, P Triantafillou, J Hayes, E Triantafillou Advances in neural information processing systems 36, 2024 | 156 | 2024 |
| Non-deterministic planning with temporally extended goals: LTL over finite and infinite traces A Camacho, E Triantafillou, C Muise, J Baier, S McIlraith Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 120 | 2017 |
| Learning a universal template for few-shot dataset generalization E Triantafillou, H Larochelle, R Zemel, V Dumoulin International conference on machine learning, 10424-10433, 2021 | 114 | 2021 |
| Inexact unlearning needs more careful evaluations to avoid a false sense of privacy J Hayes, I Shumailov, E Triantafillou, A Khalifa, N Papernot arXiv preprint arXiv:2403.01218, 2024 | 24 | 2024 |
| Ununlearning: Unlearning is not sufficient for content regulation in advanced generative ai I Shumailov, J Hayes, E Triantafillou, G Ortiz-Jimenez, N Papernot, ... arXiv preprint arXiv:2407.00106, 2024 | 19 | 2024 |
| Towards generalizable sentence embeddings E Triantafillou, J Kiros, R Urtasun, R Zemel Proceedings of the 1st Workshop on Representation Learning for NLP, 239-248, 2016 | 19 | 2016 |
| In search for a generalizable method for source free domain adaptation M Boudiaf, T Denton, B Van Merriënboer, V Dumoulin, E Triantafillou International Conference on Machine Learning, 2914-2931, 2023 | 18 | 2023 |
| Few-shot out-of-distribution detection K Wang, P Vicol, E Triantafillou, R Zemel ICML Workshop on Uncertainty and Robustness in Deep Learning 6 (7), 8, 2020 | 13 | 2020 |
| Meta-learning for semi-supervised few-shot classification E Triantafillou, H Larochelle, J Snell, J Tenenbaum, KJ Swersky, M Ren, ... International Conference on Learning Representations, 2018 | 12 | 2018 |
| A unifying framework for planning with LTL and regular expressions E Triantafillou, J Baier, S McIlraith MOCHAP@ ICAPS, 23-31, 2015 | 12 | 2015 |
| Flexible few-shot learning with contextual similarity M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ... arXiv preprint arXiv:2012.05895, 1, 2020 | 10 | 2020 |
| Non-Deterministic Planning with Temporally Extended Goals: Completing the Story for Finite and Infinite LTL (Amended Version). A Camacho, E Triantafillou, CJ Muise, JA Baier, SA McIlraith KnowProS@ IJCAI, 2016 | 10 | 2016 |
| Birds, bats and beyond: Evaluating generalization in bioacoustics models B Van Merriënboer, J Hamer, V Dumoulin, E Triantafillou, T Denton Frontiers in Bird Science 3, 1369756, 2024 | 8 | 2024 |
| What makes unlearning hard and what to do about it K Zhao, M Kurmanji, GO Bărbulescu, E Triantafillou, P Triantafillou arXiv preprint arXiv:2406.01257, 2024 | 8 | 2024 |
| Machine unlearning in learned databases: An experimental analysis M Kurmanji, E Triantafillou, P Triantafillou Proceedings of the ACM on Management of Data 2 (1), 1-26, 2024 | 8 | 2024 |
| Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition E Triantafillou, P Kairouz, F Pedregosa, J Hayes, M Kurmanji, K Zhao, ... arXiv preprint arXiv:2406.09073, 2024 | 7 | 2024 |
| BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics J Hamer, E Triantafillou, B van Merrienboer, S Kahl, H Klinck, T Denton, ... arXiv preprint arXiv:2312.07439, 2023 | 7 | 2023 |
Richard ZemelProfessor of Computer Science, University of TorontoEmail yang diverifikasi di cs.toronto.edu