Publications

Relevant Publications

* indicates equal contribution, † indicates co-correspondence

2024

• Shashata Sawmya*, Linghao Kong*, Ilia Markov, Dan Alistarh, and Nir Shavit. Sparse Expansion and Neuronal Disentanglement. arXiv:2405.15756, May 2024.
• Neehal Tumma*, Linghao Kong*†, Shashata Sawmya, Tony T. Wang, and Nir Shavit†. A connectomics-driven analysis reveals novel characterization of border regions in mouse visual cortex. bioRxiv:2024.05.24.595837, May 2024.

2023

• Tony T. Wang*, Miles Kai Wang*, Kaivu Hariharan*, and Nir Shavit. Forbidden Facts: An Investigation of Competing Objectives in Llama 2. NeurIPS 2023 ATTRIB and SOLAR Workshops, New Orleans, Louisiana, United States of America, December 2023.
• Yaron Meirovitch*†, Core Francisco Park*, Lu Mi*, Pavel Potocek*, Shashata Sawmya, Yicong Li, Yuelong Wu, Richard Schalek, Hanspeter Pfister, Remco Schoenmakers, Maurice Peemen, Jeff W. Lichtman†, Aravinthan Samuel†, and Nir Shavit†. SmartEM: Machine-Learning Guided Electron Microscopy. bioRxiv:2023.10.05.561103v1, October 2023.
• Minghao Chen, Mukesh B. Renuka, Lu Mi, Jeff Lichtman, Nir Shavit, and Yaron Meirovitch. Learning to Correct Sloppy Annotations in Electron Microscope Volumes. CVPR Workshops, pp. 4273-4284, Vancouver, Canada, June 2023.
See also bioRxiv:2020.04.30.066209.
• Yicong Li†, Yaron Meirovitch, Aaron T. Kuan, Jasper S. Phelps, Wei-Chung Allen Lee, Nir Shavit, and Lu Mi†. X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics. 2023 IEEE 20th International Symposium on Biomedical Imaging - ISBI 2023, pp. 1-5, Cartagena, Colombia, April 2023. See also arXiv:2302.00882.
• Tri Nguyen, Mukul Narwani, Mark Larson, Yicong Li, Shuhan Xie, Hanspeter Pfister, Donglai Wei, Nir Shavit, Lu Mi, Alexandra Pacureanu†, Wei-Chung Lee†, and Aaron T. Kuan†. The XPRESS challenge: Xray Projectomic Reconstruction – Extracting Segmentation with Skeletons. To appear in IEEE – ISBI 2023: International Symposium on Biomedical Imaging.
• Tony T. Wang, Igor Zablotchi, Nir Shavit, and Jonathan S. Rosenfeld. Cliff-Learning. arXiv:2302.07348, February 2023.

2022

• Lu Mi, Richard Xu, Sridhama Prakhya, Albert Lin, Nir Shavit, Aravithan D.T. Samuel†, and Srinivas C. Turaga†. Connectome-Constrained Latent Variable Models of Whole-Brain Neural Activity. Proceedings of the 10th International Conference on Learning Representations - ICLR 2022 (Virtual), April 2022.
• Lu Mi, Hao Wang, Yonglong Tian, Hao He, and Nir Shavit. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. 36th AAAI Conference on Artificial Intelligence - AAAI 2022 (Virtual), pp. 10042-10050, February 2022. Previously in ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning (Virtual), July 2021. See also arXiv:1910.04858.

2021

• Lu Mi*†, Tianxing He*†, Core Francisco Park, Hao Wang, Yue Wang, and Nir Shavit. Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework. arXiv:2110.06421, October 2021.
• Daniel Witvliet†, Ben Mulcahy*, James K. Mitchell*, Yaron Meirovitch, Daniel R. Berger, Yuelong Wu, Yufang Liu, Wan Xian Koh, Rajeev Parvathala, Douglas Holmyard, Richard L. Schalek, Nir Shavit, Andrew D. Chisholm, Jeff W. Lichtman†, Aravinthan D. T. Samuel†, and Mei Zhen†. Connectomes across development reveal principles of brain maturation in C. elegans. Nature, 596, pp. 257–261, August 2021. See also bioRxiv:2020.04.30.066209.
• Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, and Nir Shavit. On the Predictability of Pruning Across Scales. Proceedings of the 38th International Conference on Machine Learning - ICML 2021 Poster Session (Virtual), pp. 9075-9083, July 2021. PMLR 2021. See also arXiv:2006.10621.
• Lu Mi†, Hang Zhao†, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, and Dragomir Anguelov. HDMapGen: A Hierarchical Graph Generative Model of High Definition Map. Conference on Computer Vision and Pattern Recognition - CVPR 2021 (Virtual), pp. 4227-4236, June 2021. Supplementary materials. Presentation materials. See also arXiv:2106.14880.

2020

• Lu Mi, Hao Wang, Yaron Meirovitch, Richard Schalek, Srinivas C. Turaga, Jeff W. Lichtman, Aravinthan D. T. Samuel, and Nir Shavit. Learning Guided Electron Microscopy with Active Acquisition. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, pp. 77-87, Lima, Peru, October 2020. Presentation materials. See also arXiv:2101.02746.
• Faith Ellen, Rati Gelashvili, Nir Shavit, and Leqi Zhu. A complexity-based classification for multiprocessor synchronization. Distributed Computing, 33(2), pp. 125-144, August 2020. See also arXiv:1607.06139.
• Mark Kurtz*, Justin Kopinsky*, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William M. Leiserson, Sage Moore, Bill Nell, Nir Shavit, and Dan Alistarh. Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. Proceedings of the 37th International Conference on Machine Learning - ICML 2020 (Virtual), pp. 5533-5543, July 2020. PMLR 2020.
• Jonathan S., Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit. A Constructive Prediction of the Generalization Error Across Scales. Proceedings of the 8th International Conference on Learning Representations - ICLR 2020 (Virtual), April 2020. See also arXiv:1909.12673.

2019

• Yaron Meirovitch*, Lu Mi*, Hayk Saribekyan, Alexander Matveev, David Rolnick, and Nir Shavit. 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 2019, pp. 8425-8435, Long Beach, California, June 2019. See also arXiv:1812.01157.
• Daniel Witvliet, Ben Mulcahy, James K. Mitchell, Yaron Meirovitch, Daniel R. Berger, Douglas Holmyard, Richard L. Schalek, Steven J. Cook, Wan Xian Koh, Marianna Neubauer, Christine Rehaluk, Zitong Wang, David Kersen, Andrew D. Chisholm, Nir Shavit, Jeffrey W. Lichtman, Aravinthan Samuel, and Mei Zhen. Invariant, stochastic, and developmentally regulated synapses constitute the C. elegans connectome from isogenic individuals. Computational and Systems Neuroscience - Cosyne 2019 Poster Presentation, Lisbon, Portugal, March 2019.

2018

• Moritz Helmstaedter, Jeff Lichtman, and Nir Shavit. High Throughput Connectomics. Dagstuhl Seminar 18481 in Dagstuhl Reports, 8(11), pp. 112-138, November 2018.
• Dan Alistarh, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, and Nir Shavit. Inherent limitations of hybrid transactional memory. Distributed Computing, 31(3), pp. 167-185, June 2018. Previously in Proceedings of the 29th International Symposium on Distributed Computing - DISC 2015, pp. 185-199, Tokyo, Japan, May 2015. See also arXiv:1405.5689.
• Shibani Santurkar, David M. Budden, and Nir Shavit. Generative Compression. Picture Coding Symposium - PCS 2018, pp. 258-262, San Francisco, California, United States of America, June 2018. See also arXiv:1703.01467.
• Dan Alistarh, Will L. Leiserson, Alex Matveev, and Nir Shavit. ThreadScan: Automatic and Scalable Memory Reclamation. ACM Transactions on Parallel Computing - TOPC, 4(4), pp. 1-18, April 2018. Previously in Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and Architectures - SPAA 2015, pp. 123-132, Portland, Oregon, United States of America, June 2015.
• David Rolnick*, Andreas Veit*, Serge J. Belongie, and Nir Shavit. Deep Learning is Robust to Massive Label Noise. arXiv:1705.10694, February 2018.

2017

• David M. Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, and Nir Shavit. Deep Tensor Convolution on Multicores. 34th International Conference on Machine Learning - ICML 2017, pp. 615-624, Sydney, Australia, August 2017. PMLR 2017. See also arXiv:1611.06565.
• David Rolnick, Yaron Meirovitch, Toufiq Parag, Hanspeter Pfister, Viren Jain, Jeff W. Lichtman, Edward S. Boyden, and Nir Shavit. Morphological Error Detection in 3D Segmentations. arXiv:1705:10882, May 2017.
• Dan Alistarh, Will L. Leiserson., Alex Matveev, and Nir Shavit. Forkscan: Conservative Memory Reclamation for Modern Operating Systems. Proceedings of the Twelfth European Conference on Computer Systems - EuroSys 2017, pp. 483-498, Belgrade, Siberia, April 2017.
• Alexander Matveev*, Yaron Meirovitch*, Hayk Saribekyan, Wiktor Jakubiuk, Tim Kaler, Gergely Odor, David M. Budden, Aleksandar Zlateski, and Nir Shavit. A Multicore Path to Connectomics-on-Demand. 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - PPOPP 2017, pp. 267-281, Austin, Texas, February 2017.
• Shibani Santurkar, David M. Budden, Alex Matveev, Heather Berlin, Hayk Saribekyan, Yaron Meirovitch, and Nir Shavit. Toward Streaming Synapse Detection with Compositional ConvNets. arXiv:1702.07386, February 2017.

2016

• Yaron Meirovitch*, Alexander Matveev*, Hayk Saribekyan, David M. Budden, David Rolnick, Gergely Odor, Seymour Knowles-Barley, Thouis Raymond Jones, Hanspeter Pfister, Jeff William Lichtman, and Nir Shavit. A Multi-Pass Approach to Large-Scale Connectomics. arXiv:1612.02120, December 2016.
• Nir Shavit. High Throughput Connectomics (Keynote Abstract). 20th International Conference on Principles of Distributed Systems - OPODIS 2016, Madrid, Spain, December 2016.
• Nir Shavit and Gadi Taubenfeld. The computability of relaxed data structures: queues and stacks as examples. Distributed Computing, 29(5), pp. 395-407, October 2016. Previously in Structural Information and Communication Complexity - SIROCCO 2015, in the series Lecture Notes in Computer Science, 9439, pp. 414-428, https://doi.org/10.1007/978-3-319-25258-2_29, July 2015.
• Dan Alistarh, Keren Censor-Hillel, and Nir Shavit. Are Lock-Free Concurrent Algorithms Practically Wait-Free? Journal of the ACM, 63(4), pp. 1-20, September 2016. Previously in Proceedings of the ACM 46th Annual Symposium on the Theory of Computing - STOC 2014, pp, 714-723, New York, New York, United States of America, June 2014. See also arXiv:1311.3200.
• Faith Ellen, Rati Gelashvili, Nir Shavit, and Leqi Zhu. A Complexity-Based Hierarchy for Multiprocessor Synchronization (Extended Abstract). Proceedings of the ACM Symposium on Principles of Distributed Computing - PODC 2016, pp. 289-298, Chicago, Illinois, United States of America, July 2016. See also arXiv:1607.06139.
• Nir Shavit. A Multicore Path to Connectomics-on-Demand (Keynote Abstract). Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures - SPAA 2016, pp. 211, Pacific Grove, California, United States of America, July 2016.
• Nir Shavit and Alex Matveev. Transactional Memory. Encyclopedia of Algorithms, pp. 2246-2249, April 2016.

2015

• Alex Matveev, Nir Shavit, Pascal Felber, and Patrick Marlier. Read-Log-Update: A Lightweight Synchronization Mechanism for Concurrent Programming. Proceedings of the 25th ACM Symposium on Operating Systems Principles - SOSP 2015, pp. 168-183, Monterey, California, United States of America, October 2015.
• Yehuda Afek, Alex Matveev, Oscar R. Moll, and Nir Shavit. Amalgamated Lock-Elision. Proceedings of the 29th International Symposium on Distributed Computing - DISC 2015, pp. 309-324, Tokyo, Japan, May 2015.
• Alex Matveev and Nir Shavit. Reduced Hardware NOrec: A Safe and Scalable Hybrid Transactional Memory. Proceedings of the 20th International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS 2015, pp. 59-71, Istanbul, Turkey, March 2015.
• Dan Alistarh, Justin Kopinsky, Jerry Li, and Nir Shavit. The SprayList: A Scalable Relaxed Priority Queue. Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - PPOPP 2015, pp. 11-20, San Francisco, California, United States of America, February 2015.
• David Dice, Virendra J. Marathe, and Nir Shavit. Lock Cohorting: A General Technique for Designing NUMA Locks. ACM Transactions on Parallel Computing - TOPC, 1(2), pp. 1-42, February 2015.

2014

• Rati Gelashvili, Mohsen Ghaffari, Jerry Li, and Nir Shavit. On the Importance of Registers for Computability. Proceedings of the 18th International Conference on Principles of Distributed Systems - OPODIS 2014, Cortina d'Amprezzo, Italy, December 2014. See also arXiv:1411.0168.
• Jeff Lichtman, Hanspeter Pfister, and Nir Shavit The big data challenges of connectomics. Nature Neuroscience, 17(11), pp. 1448-1454, November 2014.
• Zeyuan Allen-Zhu, Rati Gelashvili, Silvio Micali, and Nir Shavit. Sparse sign-consistent Johnson-Lindenstrauss matrices: Compression with neuroscience-based constraints. Proceedings of the National Academy of Sciences - PNAS, 111(47), pp. 16872-16876, October 2014. See also arXiv:1411.5383.
• Dan Alistarh, Keren Censor-Hillel, and Nir Shavit. Brief announcement: are lock-free concurrent algorithms practically wait-free? Proceedings of the 33rd Annual ACM Symposium on Principles of Distributed Computing - PODC 2014, pp. 50-52, Paris, France, July 2014.
• Dan Alistarh, Oksana Denysyuk, Luis E.T. Rodrigues, and Nir Shavit. Balls-into-Leaves: Sub-logarithmic Renaming in Synchronous Message-Passing Systems. Proceedings of the 33rd Annual ACM Symposium on Principles of Distributed Computing - PODC 2014, pp. 232-241, Paris, France, July 2014.
• Dan Alistarh, Justin Kopinsky, Alex Matveev, and Nir Shavit. The LevelArray: A Fast, Practical Long-Lived Renaming Algorithm. Proceedings of the 34th Annual International Conference on Distributed Computing Systems - ICDCS 2014, Madrid, Spain, July 2014. See also arXiv:1405.5461.
• David Dice, Virendra J. Marathe, and Nir Shavit. Persistent Unfairness Arising From Cache Residency Imbalance. Proceedings of the 26th Annual ACM Symposium on Parallelism in Algorithms and Architectures - SPAA 2014, pp. 82-83, Prague, Czech Republic, June 2014.
• Dan Alistarh, Patrick Eugster, Maurice Herlihy, Alex Matveev, and Nir Shavit. StackTrack: An Automated Transactional Approach to Concurrent Memory Reclamation. Proceedings of the 9th European Conference on Computer Systems - EuroSys 2014, pp. 1-14, Amsterdam, The Netherlands, April 2014.

All of Nir's Publications

List of publications