• Wang, Tony T., Wang, Miles Kai, Hariharan, Kaivu, Shavit, Nir. Forbidden Facts: An Investigation of Competing Objectives in Llama 2. To appear in NeurIPS 2023 ATTRIB and SOLAR Workshops, December 2023.
• Meirovitch, Yaron, Park, Core Francisco, Mi, Lu, Potocek, Pavel, Sawmya, Shashata, Li, Yicong, Wu, Yuelong, Schalek, Richard, Pfister, Hanspeter, Schoenmakers, Remco, Peemen, Maurice, Lichtman, Jeff W., Samual, Aravinthan, Shavit, Nir. SmartEM: Machine-Learning Guided Electron Microscopy. bioRxiv: 2023.10.05.561103v1, October 2023.
• Li, Yicong, Meirovitch, Yaron, Kuan, Aaron T., Phelps, Jasper S., Pacureanu, Alexandra, Lee, Wei-Chung Allen, Shavit, Nir, Mi, Lu. X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics. IEEE – ISBI 2023: International Symposium on Biomedical Imaging, April 2023. Poster Link Video Link
• Nguyen, Tri, Narwani, Mukul, Larson, Mark, Li, Yicong, Xie, Shuhan, Pfister, Hanspeter, Wei, Donglai, Shavit, Nir, Mi, Lu, Pacureanu, Alexandra, Lee, Wei-Chung, Kuan, Aaron T. The XPRESS challenge: Xray Projectomic Reconstruction – Extracting Segmentation with Skeletons. IEEE – ISBI 2023: International Symposium on Biomedical Imaging, April 2023.
• Wang, Tony T., Zablotchi, Igor, Shavit, N., Rosenfeld, Jonathan S. Cliff-Learning. arXiv:2302.07348, February 2023.
• Mi, Lu. Deep Learning Tools for Next-Generation Connectomics. PhD thesis, MIT Department of Electrical Engineering and Computer Science, Cambridge, MA., August 2022. Slides.
• Mi, Lu, Wang, Hao, Tian, Yonglong, He, Hao, Shavit, Nir. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. 36th AAAI Conference on Artificial Intelligence- A virtual conference, February 22-March 1, 2022.
• Mi, Lu, Xu, Richard, Prakhya, Sridhama, Lin, Albert, Shavit, Nir, Samuel, Aravithan D.T. and Turaga, Srinivas C. Connectome-Constrained Latent Variable Models of Whole-Brain Neural Activity. Tenth International Conference on Learning Representations- ICLR 2022 (Virtual), April 2022.
• Witvliet, Daniel, Mulcahy, Ben, Mitchell, James K., Meirovitch, Yaron, Berger, Daniel R., Wu, Yuelong, Liu, Yufang, Koh, Wan Xian, Parvathala, Rajeev, Holmyard, Douglas, Schalek, Richard L., Shavit, Nir, Chisholm, Andrew D., Lichtman, Jeff W., Samuel, Aravinthan D.T., and Zhen, Mei. Connectomes across development reveal principles of brain maturation in C. elegans. Nature, 596, pages 257–261, 2021. Also, bioRxiv 2020.04.30.066209 https://doi.org/10.1101/2020.04.30.066209.
• Rosenfeld, Jonathan S., Frankle, Jonathan, Carbin, Michael, and Shavit, Nir. On the Predictability of Pruning Across Scales. ICML 2021 Poster Session. Also, arXiv:2006.10621, June 2020.
• Mi, Lu, Wang, Hao, Tian, Yonglong, and Shavit, Nir. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. ICML Workshop on Uncertainty and Robustness in Deep Learning, July 2021.
• Mi, Lu, Hang Zhao, Hang, Nash, Charlie, Jin, Xiaohan, Gao, Jiyang, Sun, Chen, Schmid, Cordelia, Shavit, Nir, Chai, Yuning, Anguelov, Dragomir. HDMapGen: A Hierarchical Graph Generative Model of High Definition Map. Conference on Computer Vision and Pattern Recognition (CVPR 2021), June 2021. Supplementary Materials. Video, poster, and slides. Also, CoRR abs/2106.14880, 2021.
• Mi, Lu, Wang, Hao, Meirovitch, Yaron, Schalek, Richard, Turaga, Srinivas C., Lichtman, Jeff W., Samuel, Aravinthan D.T, Shavit, N. Learning Guided Electron Microscopy with Active Acquisition. CoRR abs/2101.02746, 2021.

  Mi, Lu, He, Tianxing, Park, Core Francisco, Wang, Hao, Wang, Yue, Shavit, Nir. Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework. CoRR abs/2110.06421, 2021.

• Mi, Lu, Wang, Hao, Meirovitch, Yaron, Schalek, Richard, Turaga, Srinivas C., Lichtman, Jeff W., Samuel, Aravinthan D. T. and Shavit, Nir. Learning Guided Electron Microscopy with Active Acquisition. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), October 2020. Presentation materials.
• Rosenfeld, Jonathan S., Frankle, Jonathan, Carbin, Michael, and Shavit, Nir. On the Predictability of Pruning Across Scales. arXiv:2006.10621, June 2020.
• Rosenfeld, Jonathan S., Rosenfeld, Amir, Belinkov, Yonatan and Shavit, Nir. A Constructive Prediction of the Generalization Across Scales by , ICLR 2020. This paper has been featured in Andrew Ng’s news, The Batch.
• Witvliet, Daniel, Mulcahy, Ben, Mitchell, James K., Meirovitch, Yaron, Berger, Daniel R., Wu, Yuelong, Liu, Yufang, Koh, Wan Xian, Parvathala, Rajeev, Holmyard, Douglas, Schalek, Richard L., Shavit, Nir, Chisholm, Andrew D., Lichtman, Jeff W., Samuel, Aravinthan D.T., and Zhen, Mei. Connectomes across development reveal principles of brain maturation in C. elegans. bioRxiv 2020.04.30.066209 https://doi.org/10.1101/2020.04.30.066209.
• Kurtz, Mark, Kopinsky, Justin, Gelashvili, Rati, Matveev, Alexander, Carr, John, Goin, Michael, Leiserson, William M., Moore, Sage, Shavit, Nir, Alistarh, Dan.
  Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), Proceedings of Machine Learning Research 119, PMLR 2020, pages 5533-5543, July 2020.

• Mi, Lu, Wang, Hao, Tian, Yonglong, and Shavit, Nir. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. arXiv: 1910.04858, 2019.
• Rosenfeld, Jonathan S., Rosenfeld, Amir, Belinkov, Yonatan, and Shavit, Nir. A Constructive Prediction of the Generalization Error Across Scales. 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, David, 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.

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