| On the Modularity of Hypernetworks T Galanti, L Wolf Advances in Neural Information Processing Systems 33, 10409-10419, 2020 | 85 | 2020 |
| Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models C Xu, S Yang, T Galanti, B Wu, X Yue, B Zhai, W Zhan, P Vajda, K Keutzer, ... European Conference on Computer Vision, 638-656, 2022 | 84 | 2022 |
| On the Role of Neural Collapse in Transfer Learning T Galanti, A György, M Hutter International Conference on Learning Representations (ICLR), 2021 | 80 | 2021 |
| Evaluation metrics for conditional image generation Y Benny, T Galanti, S Benaim, L Wolf International Journal of Computer Vision 129, 1712-1731, 2021 | 52 | 2021 |
| Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neural collapse and generalization bounds M Xu, A Rangamani, Q Liao, T Galanti, T Poggio Research, 2023 | 44* | 2023 |
| Emerging Disentanglement in Auto-Encoder based Unsupervised Image Content Transfer O Press, T Galanti, S Benaim, L Wolf International Conference on Learning Representations (ICLR), 2018 | 43 | 2018 |
| A Theoretical Framework for Deep Transfer Learning T Galanti, L Wolf, T Hazan Information and Inference: A Journal of the IMA 5 (2), 159-209, 2016 | 43 | 2016 |
| SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network T Galanti, ZS Siegel, A Gupte, TA Poggio NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning, 2024 | 39* | 2024 |
| Feature learning in deep classifiers through intermediate neural collapse A Rangamani, M Lindegaard, T Galanti, TA Poggio International Conference on Machine Learning, 28729-28745, 2023 | 39 | 2023 |
| On Infinite-Width Hypernetworks E Littwin*, T Galanti*, L Wolf, G Yang, (*equal contributions) Advances in Neural Information Processing Systems 33, 2020 | 32* | 2020 |
| Reverse engineering self-supervised learning I Ben-Shaul, R Shwartz-Ziv, T Galanti, S Dekel, Y LeCun Advances in Neural Information Processing Systems 36, 58324-58345, 2023 | 30 | 2023 |
| The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings T Galanti, L Wolf, S Benaim International Conference on Learning Representations (ICLR), 2017 | 30* | 2017 |
| A Formal Approach to Explainability L Wolf, T Galanti, T Hazan AAAI/ACM Conference on Artificial Intelligence, Ethics and Society 2, 2019 | 29 | 2019 |
| Domain Intersection and Domain Difference S Benaim, M Khaitov, T Galanti, L Wolf Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 27 | 2019 |
| Comparative generalization bounds for deep neural networks T Galanti, L Galanti, I Ben-Shaul Transactions on Machine Learning Research, 2023 | 26* | 2023 |
| Generalization bounds for transfer learning with pretrained classifiers T Galanti, A György, M Hutter arXiv preprint arXiv:2212.12532, 2022 | 16 | 2022 |
| Improved Generalization Bounds for Transfer Learning via Neural Collapse T Galanti, A György, M Hutter First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at …, 2022 | 15 | 2022 |
| Estimating the success of unsupervised image to image translation S Benaim*, T Galanti*, L Wolf, (*equal contributions) Proceedings of the European Conference on Computer Vision (ECCV), 218-233, 2018 | 12 | 2018 |
| Meta internal learning R Bensadoun, S Gur, T Galanti, L Wolf Advances in Neural Information Processing Systems 34, 20645-20656, 2021 | 11 | 2021 |
| Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs T Galanti, S Benaim, L Wolf Journal of Machine Learning Research 22, 90:1-90:42, 2021 | 9* | 2021 |
Lior WolfThe School of Computer Science at Tel Aviv UniversityVerified email at cs.tau.ac.il
