| 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 | 1322 | 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 | 538 | 2019 |
| Few-shot learning through an information retrieval lens E Triantafillou, R Zemel, R Urtasun Advances in neural information processing systems 30, 2017 | 246 | 2017 |
| 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 | 91 | 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 | 62 | 2021 |
| 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 |
| A unifying framework for planning with LTL and regular expressions E Triantafillou, J Baier, S McIlraith MOCHAP@ ICAPS, 23-31, 2015 | 11 | 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 | 7 | 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 | 7 | 2016 |
| Few-shot out-of-distribution detection K Wang, P Vicol, E Triantafillou, R Zemel ICML Workshop on Uncertainty and Robustness in Deep Learning, 2020 | 6 | 2020 |
| Learning flexible classifiers with Shot-CONditional episodic (SCONE) training E Triantafillou, V Dumoulin, H Larochelle, R Zemel | 3 | 2020 |
| In Search for a Generalizable Method for Source Free Domain Adaptation M Boudiaf, T Denton, B van Merriënboer, V Dumoulin, E Triantafillou arXiv preprint arXiv:2302.06658, 2023 | 2 | 2023 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. September 2019 E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... URL https://openreview. net/forum, 0 | 2 | |
| Few-Shot Attribute Learning M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ... | 1 | 2021 |
| Towards Strong Generalization from Few Examples E Triantafillou University of Toronto (Canada), 2021 | 1 | 2021 |
| Out-of-distribution Detection in Few-shot Classification KC Wang, P Vicol, E Triantafillou, CC Liu, R Zemel | 1 | 2019 |
| Few-shot learning for free by modelling global class structure X Li, W Grathwohl, E Triantafillou, D Duvenaud, R Zemel 2nd Workshop on Meta-Learning at NeurIPS, 2018 | 1 | 2018 |
| Towards Unbounded Machine Unlearning M Kurmanji, P Triantafillou, E Triantafillou arXiv preprint arXiv:2302.09880, 2023 | | 2023 |
| The Brainy Student: Scalable Unlearning by Selectively Disobeying the Teacher M Kurmanji, P Triantafillou, E Triantafillou | | 2022 |
| NOTELA: A Generalizable Method for Source Free Domain Adaptation M Boudiaf, B van Merrienboer, V Dumoulin, E Triantafillou | | 2022 |
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
