| Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments M Marks, Q Jin, O Sturman, L von Ziegler, S Kollmorgen, ... Nature machine intelligence 4 (4), 331-340, 2022 | 101* | 2022 |
| Non-invasive molecularly-specific millimeter-resolution manipulation of brain circuits by ultrasound-mediated aggregation and uncaging of drug carriers MS Ozdas, AS Shah, PM Johnson, N Patel, M Marks, TB Yasar, U Stalder, ... Nature communications 11 (1), 4929, 2020 | 57 | 2020 |
| Text-image alignment for diffusion-based perception N Kondapaneni*, M Marks*, M Knott*, R Guimaraes, P Perona Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 25 | 2024 |
| A Foundation Model for Cell Segmentation U Israel*, M Marks*, R Dilip*, Q Li, MS Schwartz, E Pradhan, E Pao, S Li, ... bioRxiv, 2023.11. 17.567630, 2023 | 22* | 2023 |
| An optimized registration workflow and standard geometric space for small animal brain imaging HI Ioanas, M Marks, V Zerbi, MF Yanik, M Rudin NeuroImage 241, 118386, 2021 | 19* | 2021 |
| MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior JJ Sun*, M Marks*, AW Ulmer, D Chakraborty, B Geuther, E Hayes, H Jia, ... International Conference on Machine Learning (ICML), 2023 | 15* | 2023 |
| SAMRI—Small Animal Magnetic Resonance Imaging, November 2017 HI Ioanas, M Marks, D Schmidt, F Aymanns, M Rudin URL https://doi. org/10.5281/zenodo 1044033, 0 | 11* | |
| An automated open-source workflow for standards-compliant integration of small animal magnetic resonance imaging data HI Ioanas, M Marks, CM Garin, M Dhenain, MF Yanik, M Rudin Frontiers in neuroinformatics 14, 5, 2020 | 9* | 2020 |
| Anatomically distributed neural representations of instincts in the hypothalamus S Stagkourakis, G Spigolon, M Marks, M Feyder, J Kim, P Perona, ... bioRxiv, 2023 | 4* | 2023 |
| A multicomponent approach to studying cultural propensities during foraging in the wild KR Mannion, EF Ballare, M Marks, T Gruber Journal of Animal Ecology, 2022 | 3 | 2022 |
| A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification M Marks*, M Knott*, N Kondapaneni, E Cole, T Defraeye, F Perez-Cruz, ... arXiv preprint arXiv:2407.12210, 2024 | 2 | 2024 |
| Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI RLEM Ubaghs, R Boehringer, M Marks, HK Hesse, MF Yanik, V Zerbi, ... bioRxiv, 2023.11. 14.566368, 2023 | 2 | 2023 |
| Robust Disentanglement of a Few Factors at a Time using rPU-VAE B Estermann*, M Marks*, MF Yanik Advances in Neural Information Processing Systems (NeurIPS), 2020 | 2 | 2020 |
| Less is More: Discovering Concise Network Explanations N Kondapaneni, M Marks, O Mac Aodha, P Perona ICLR 2024 Workshop on Representational Alignment, 2024 | 1 | 2024 |
| Predicting cardiac remodeling after myocardial infarction with machine learning: are we there yet? SJ Reinstadler, C Dlaska, M Reindl, M Marks International Journal of Cardiology 355, 6-7, 2022 | 1 | 2022 |
| Probing the Mid-level Vision Capabilities of Self-Supervised Learning X Chen, M Marks, Z Cheng arXiv preprint arXiv:2411.17474, 2024 | | 2024 |
| Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision D Khalil, C Liu, P Perona, JJ Sun, M Marks arXiv preprint arXiv:2409.09455, 2024 | | 2024 |
| Comparing Neural Networks and Human Subjects in Assessing Trademark Similarities S Shimojo, FM Toma, M Noguchi, E Cole, M Marks, M Shehata, DA Wu Journal of Vision 23 (9), 5553-5553, 2023 | | 2023 |
| The Effect of Somatostatin+ Interneurons on the Negative BOLD Response. RLEM Ubaghs, R Böhringer, M Marks, MF Yanik, BF Grewe | | |
| Shaping the BOLD signal through Excitatory and Inhibitory Interaction. RLEM Ubaghs, H Dermutz, R Böhringer, M Marks, MF Yanik, BF Grewe | | |
Mehmet Fatih YanikETH ZurichVerified email at ethz.ch
