| 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 | 429 | 2019 |
| Rigging the lottery: Making all tickets winners U Evci, T Gale, J Menick, PS Castro, E Elsen International Conference on Machine Learning, 2943-2952, 2020 | 256 | 2020 |
| Empirical analysis of the hessian of over-parametrized neural networks L Sagun, U Evci, VU Guney, Y Dauphin, L Bottou arXiv preprint arXiv:1706.04454, 2017 | 255 | 2017 |
| The difficulty of training sparse neural networks U Evci, F Pedregosa, A Gomez, E Elsen arXiv preprint arXiv:1906.10732, 2019 | 63 | 2019 |
| Gradient flow in sparse neural networks and how lottery tickets win U Evci, Y Ioannou, C Keskin, Y Dauphin Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6577-6586, 2022 | 29 | 2022 |
| A practical sparse approximation for real time recurrent learning J Menick, E Elsen, U Evci, S Osindero, K Simonyan, A Graves arXiv preprint arXiv:2006.07232, 2020 | 26* | 2020 |
| Comparing transfer and meta learning approaches on a unified few-shot classification benchmark V Dumoulin, N Houlsby, U Evci, X Zhai, R Goroshin, S Gelly, H Larochelle arXiv preprint arXiv:2104.02638, 2021 | 21* | 2021 |
| Head2toe: Utilizing intermediate representations for better transfer learning U Evci, V Dumoulin, H Larochelle, MC Mozer International Conference on Machine Learning, 6009-6033, 2022 | 20 | 2022 |
| Gradmax: Growing neural networks using gradient information U Evci, M Vladymyrov, T Unterthiner, B van Merriënboer, F Pedregosa arXiv preprint arXiv:2201.05125, 2022 | 10 | 2022 |
| Detecting dead weights and units in neural networks U Evci arXiv preprint arXiv:1806.06068, 2018 | 4 | 2018 |
| The State of Sparse Training in Deep Reinforcement Learning L Graesser, U Evci, E Elsen, PS Castro International Conference on Machine Learning, 7766-7792, 2022 | 1 | 2022 |
| One Step from the Locomotion to the Stepping Pattern R Boulic, U Evci, E Molla, P Pisupati Proceedings of the 29th International Conference on Computer Animation and …, 2016 | 1 | 2016 |
| Training Recipe for N: M Structured Sparsity with Decaying Pruning Mask A Yazdanbakhsh, SC Kao, S Agrawal, S Subramanian, T Krishna, U Evci | | 2022 |
| Mean Replacement Pruning U Evci, N Le Roux, P Castro, L Bottou | | |
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