| Space–time wiring specificity supports direction selectivity in the retina JS Kim, MJ Greene, A Zlateski, K Lee, M Richardson, SC Turaga, ... Nature 509 (7500), 331-336, 2014 | 552 | 2014 |
| Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity NL Turner, T Macrina, JA Bae, R Yang, AM Wilson, C Schneider-Mizell, ... Cell 185 (6), 1082-1100. e24, 2022 | 143 | 2022 |
| Oligodendrocyte precursor cells ingest axons in the mouse neocortex JA Buchanan, L Elabbady, F Collman, NL Jorstad, TE Bakken, C Ott, ... Proceedings of the National Academy of Sciences 119 (48), e2202580119, 2022 | 117 | 2022 |
| Binary and analog variation of synapses between cortical pyramidal neurons S Dorkenwald, NL Turner, T Macrina, K Lee, R Lu, J Wu, AL Bodor, ... Elife 11, e76120, 2022 | 116 | 2022 |
| On the importance of label quality for semantic segmentation A Zlateski, R Jaroensri, P Sharma, F Durand Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 106 | 2018 |
| Recursive training of 2D-3D convolutional networks for neuronal boundary prediction K Lee, A Zlateski, V Ashwin, HS Seung Advances in Neural Information Processing Systems 28, 2015 | 94 | 2015 |
| Structure and function of axo-axonic inhibition CM Schneider-Mizell, AL Bodor, F Collman, D Brittain, A Bleckert, ... Elife 10, e73783, 2021 | 72 | 2021 |
| Optimizing N-dimensional, winograd-based convolution for manycore CPUs Z Jia, A Zlateski, F Durand, K Li Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of …, 2018 | 66 | 2018 |
| ZNN--A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-core and Many-Core Shared Memory Machines A Zlateski, K Lee, HS Seung 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2016 | 61 | 2016 |
| Image segmentation by size-dependent single linkage clustering of a watershed basin graph A Zlateski, HS Seung arXiv preprint arXiv:1505.00249, 2015 | 52 | 2015 |
| Chandelier cell anatomy and function reveal a variably distributed but common signal CM Schneider-Mizell, AL Bodor, F Collman, D Brittain, AA Bleckert, ... BioRxiv, 2020.03. 31.018952, 2020 | 41 | 2020 |
| Multiscale and multimodal reconstruction of cortical structure and function NL Turner, T Macrina, JA Bae, R Yang, AM Wilson, C Schneider-Mizell, ... Biorxiv, 2020.10. 14.338681, 2020 | 40 | 2020 |
| ZNN i: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs A Zlateski, K Lee, HS Seung Proceedings of the International Conference for High Performance Computing …, 2016 | 35* | 2016 |
| The anatomy of efficient FFT and winograd convolutions on modern CPUs A Zlateski, Z Jia, K Li, F Durand Proceedings of the ACM International Conference on Supercomputing, 414-424, 2019 | 32 | 2019 |
| Automated computation of arbor densities: a step toward identifying neuronal cell types U Sümbül, A Zlateski, A Vishwanathan, RH Masland, HS Seung Frontiers in neuroanatomy 8, 139, 2014 | 31 | 2014 |
| A multicore path to connectomics-on-demand A Matveev, Y Meirovitch, H Saribekyan, W Jakubiuk, T Kaler, G Odor, ... Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of …, 2017 | 25 | 2017 |
| FFT convolutions are faster than Winograd on modern CPUs, here is why A Zlateski, Z Jia, K Li, F Durand arXiv preprint arXiv:1809.07851, 2018 | 20 | 2018 |
| System and method of executing neural networks A Matveev, N Shavit US Patent 10,832,133, 2020 | 19 | 2020 |
| Compile-time optimized and statically scheduled ND convnet primitives for multi-core and many-core (Xeon Phi) CPUs A Zlateski, HS Seung Proceedings of the International Conference on Supercomputing, 1-10, 2017 | 16 | 2017 |
| Systems and methods for generation of sparse code for convolutional neural networks A Zlateski, J Kopinsky US Patent 11,216,732, 2022 | 15 | 2022 |
