| Sub-image anomaly detection with deep pyramid correspondences N Cohen, Y Hoshen arXiv preprint arXiv:2005.02357, 2020 | 501 | 2020 |
| PANDA--Adapting Pretrained Features for Anomaly Detection T Reiss*, N Cohen*, L Bergman, Y Hoshen Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 281* | 2020 |
| Deep nearest neighbor anomaly detection L Bergman*, N Cohen*, Y Hoshen arXiv preprint arXiv:2002.10445, 2020 | 168 | 2020 |
| RNA structural determinants of optimal codons revealed by MAGE-Seq ED Kelsic, H Chung, N Cohen, J Park, HH Wang, R Kishony Cell systems 3 (6), 563-571. e6, 2016 | 75 | 2016 |
| “This is my unicorn, Fluffy”: Personalizing frozen vision-language representations N Cohen, R Gal, EA Meirom, G Chechik, Y Atzmon European conference on computer vision, 558-577, 2022 | 67 | 2022 |
| An image is worth more than a thousand words: Towards disentanglement in the wild A Gabbay, N Cohen, Y Hoshen Advances in Neural Information Processing Systems 34, 9216-9228, 2021 | 37 | 2021 |
| Anomaly detection requires better representations T Reiss, N Cohen, E Horwitz, R Abutbul, Y Hoshen European Conference on Computer Vision, 56-68, 2022 | 25 | 2022 |
| Circumventing concept erasure methods for text-to-image generative models M Pham, KO Marshall, N Cohen, G Mittal, C Hegde The Twelfth International Conference on Learning Representations, 2023 | 15 | 2023 |
| Improving zero-shot models with label distribution priors J Kahana, N Cohen, Y Hoshen arXiv preprint arXiv:2212.00784, 2022 | 15 | 2022 |
| Disentanglement of single-cell data with biolord Z Piran, N Cohen, Y Hoshen, M Nitzan Nature Biotechnology, 1-6, 2024 | 11 | 2024 |
| Set Features for Anomaly Detection N Cohen, I Tzachor, Y Hoshen arXiv preprint arXiv:2311.14773, 2023 | 10* | 2023 |
| Chip-scale atomic wave-meter enabled by machine learning E Edrei, N Cohen, E Gerstel, S Gamzu-Letova, N Mazurski, U Levy Science advances 8 (15), eabn3391, 2022 | 8 | 2022 |
| Out-of-distribution detection without class labels N Cohen, R Abutbul, Y Hoshen European Conference on Computer Vision, 101-117, 2022 | 7 | 2022 |
| TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks B Feuer, RT Schirrmeister, V Cherepanova, C Hegde, F Hutter, ... arXiv preprint arXiv:2402.11137, 2024 | 6 | 2024 |
| Red PANDA: Disambiguating image anomaly detection by removing nuisance factors N Cohen, J Kahana, Y Hoshen The Eleventh International Conference on Learning Representations, 2023 | 6* | 2023 |
| Scaling tabpfn: Sketching and feature selection for tabular prior-data fitted networks B Feuer, N Cohen, C Hegde NeurIPS 2023 Second Table Representation Learning Workshop, 2023 | 5 | 2023 |
| Language-Guided Image Clustering N Cohen, Y Hoshen | 4* | |
| Robust Concept Erasure Using Task Vectors M Pham, KO Marshall, C Hegde, N Cohen arXiv preprint arXiv:2404.03631, 2024 | 3 | 2024 |
| Detecting anomalous proteins using deep representations T Michael-Pitschaze, N Cohen, D Ofer, Y Hoshen, M Linial NAR Genomics and Bioinformatics 6 (1), lqae021, 2024 | 3 | 2024 |
| No free lunch: The hazards of over-expressive representations in anomaly detection T Reiss, N Cohen, Y Hoshen arXiv preprint arXiv:2306.07284, 2023 | 3 | 2023 |
