Yoli Shavit

Yoli Shavit

Senior Research Scientist @ NVIDIA | Postdoctoral Researcher @ Bar-Ilan University

List of Publications

  1. Y. Shavit, R. Ferens, and Y. Keller, “Coarse-to-fine multi-scene pose regression with transformers,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
  2. Y. Shavit and Y. Keller, “Camera pose auto-encoders for improving pose regression,” in European Conference on Computer Vision (ECCV), pp. 140–157, Springer, 2022.
  3. J. Liao, Y. Ding, Y. Shavit, D. Huang, S. Ren, J. Guo, W. Feng, and K. Zhang, “Wt-MVSNet: window-based transformers for multi-view stereo,” Advances in Neural Information Processing Systems (NIPS), vol. 35, pp. 8564–8576, 2022.
  4. Y. Shi, J.-X. Cai, Y. Shavit, T.-J. Mu, W. Feng, and K. Zhang, “ClusterGNN: Cluster-based coarse-to-fine graph neural network for efficient feature matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12517–12526, 2022.
  5. W. Fang, K. Zhang, Y. Shavit, and W. Feng, “Adversarial learning of hard positives for place recognition,” in 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–7, IEEE, 2022.
  6. Y. Shavit, R. Ferens, and Y. Keller, “Learning multi-scene absolute pose regression with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2733–2742, 2021. [Oral Presentation]
  7. Y. Shavit and I. Klein, “Boosting inertial-based human activity recognition with transformers,” IEEE Access, vol. 9, pp. 53540–53547, 2021.
  8. Y. Shavit and R. Ferens, “Do we really need scene-specific pose encoders?,” in 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3186–3192, IEEE, 2021.
  9. Y. Shavit and R. Ferens, “Introduction to camera pose estimation with deep learning,” arXiv preprint arXiv:1907.05272, 2019. [58 citations]
  10. Y. Shavit, B. J. Walker, and P. Lio’, “Hierarchical block matrices as efficient representations of chromosome topologies and their application for 3c data integration,” Bioinformatics, vol. 32, no. 8, pp. 1121–1129, 2016.
  11. Y. Shavit, B. Yordanov, S.-J. Dunn, C. M. Wintersteiger, T. Otani, Y. Hamadi, F. J. Livesey, and H. Kugler, “Automated synthesis and analysis of switching gene regulatory networks,” Biosystems, vol. 146, pp. 26–34, 2016.
  12. F. K. Hamey, Y. Shavit, V. Maciulyte, C. Town, P. Li`o, and S. Tosi, “Automated detection of fluorescent probes in molecular imaging,” in Computational Intelligence Methods for Bioinformatics and Biostatistics: 11th International Meeting, CIBB 2014, Cambridge, UK, June 26-28, 2014, Revised Selected Papers, pp. 68-75, Springer, 2015.
  13. Y. Shavit, I. Merelli, L. Milanesi, and P. Lio’, “How computer science can help in understanding the 3D genome architecture,” Briefings in Bioinformatics, vol. 17, pp. 733–744, 10 2015.
  14. Y. Shavit, F. K. Hamey, and P. Lio’, “Fishical: an r package for iterative fish-based calibration of hi-c data,” Bioinformatics, vol. 30, no. 21, pp. 3120–3122, 2014.
  15. Y. Shavit and P. Lio’, “Combining a wavelet change point and the bayes factor for analysing chromosomal interaction data,” Molecular BioSystems, vol. 10, no. 6, pp. 1576–1585, 2014.
  16. Y. Shavit and P. Lio’, “Cytohic: a cytoscape plugin for visual comparison of hi-c networks,” Bioinformatics, vol. 29, no. 9, pp. 1206–1207, 2013.
  17. E. Mashiach, D. Schneidman-Duhovny, A. Peri, Y. Shavit, R. Nussinov, and H. J. Wolfson, “An integrated suite of fast docking algorithms,” Proteins: Structure, Function, and Bioinformatics, vol. 78, no. 15, pp. 3197–3204, 2010.