Publications – Computational Connectomics Group

Mi, Lu, Wang, Hao, Tian, Yonglong, and Shavit, Nir. Training-Free Uncertainty Estimation for Neural Networks. arXiv: 1910.04858, 2019.
Rosenfeld, Jonathan S., Rosenfeld, Amir, Belinkov, Yonatan, and Shavit, Nir. A Constructive Prediction of the Generalization Error Across Scales. To appear in Proceedings of the International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, April 2020. Also, arXiv:1909.12673, September 2019.
Meirovitch, Yaron, Mi, Lu, Saribekyan, Hayk, Matveev, Alexander, Rolnick, Rolnick, and Shavit, Nir. Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8425-8435, 2019.
Witvliet, Daniel, Mulcahy, Ben, Mitchell, James K., Meirovitch, Yaron, Berger, Daniel R., Holmyard, Douglas, Schalek, Richard L., Cook, Steven J., Xian Koh, Wan, Neubauer, Marianna, Rehaluk, Christine, Wang, ZiTong, Kersen, David, Chisholm, Andrew D., Shavit, Nir, Lichtman, Jeffrey W., Samuel, Aravinthan, and Zhen, Mei. Invariant, stochastic, and developmentally regulated synapses constitute the C. elegans connectome from isogenic individuals. Poster Presentation at Cosyne 2019.
Meirovitch, Yaron, Mi, Lu, Saribekyan, Hayk, Matveev, Alexander, Rolnick, David, Wierzynski, Casimir, and Shavit, Nir. Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. CoRR abs/1812.01157, 2018.
Santurkar, Shibani, Budden, David M., and Shavit, Nir. Generative Compression. PCS 2018. Also, CoRR.abs/1703.01467, 2017.
Budden, David, Matveev, Alexander, Santurkar, Shibani, Chaudhuri, Shraman Ray, and Shavit, Nir. Deep Tensor Convolution on Multicores. ICML 2017. Also, CoRR abs/1611.06565, 2016.
Matveev, A., Meirovitch, Y., Saribekyan, H., Jakubiuk, W., Kaler, T., Odor, G., Budden, D., Zlateski, A., and Shavit, N. A Multicore Path to Connectomics-on-Demand. PPoPP 2017 (Best Paper Nominee).
Rolnick, David, Meirovitch, Yaron, Parag, Toufiq, Pfister, Hanspeter, Jain, Vien, Lichtman, Jeff W., Boyden, Edward S., and Shavit, Nir. Morphological error detection in 3d segmentations. CoRR.abs/1705.10882, 2017.
Rolnick, David, Veit, Andreas, Belongie, Serge J., and Shavit, Nir. Deep Learning is Robust to Massive Label Noise. CoRR abs/1705.10694, 2017.
Santurkar, Shibani, Budden, David, Matveev, Alexander, Berlin, Heather, Saribekyan, Hayk, Meirovitch, Yaron, and Shavit, Nir. Toward Streaming Synapse Detection with Compositional ConvNets. CoRR abs/1702.07386, 2017.
Meirovitch, Y., Matveev, A., Saribekyan, H., Budden, D., Rolnick, D., Odor, G., Knowles-Barley, S., Thouis, R., Pfister, H., Lichtman, J., Shavit, N.A Multi-Pass Approach to Large-Scale Connectomics. CoRR abs/1612.02120, 2016.
Shavit, Nir. A Multicore Path to Connectomics-on-Demand. SPAA 2016.
Lichtman, J., Pfister, H., and Shavit, N. The big data challenges of connectomics. Nature Neuroscience, 17, pp. 1448-1454, November 2014.
Allen-Zhu, Zeyuan, Gelashvili, Rati, Micali, Silvio, and Shavit, Nir. Sparse sign-consistent Johnson-Lindenstrauss matrices: Compression with neuroscience-based constraints. Proceedings of the National Academy of Sciences USA; 111(47), pp. 16872-16876, October 2014.