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Aleksandar Zlateski
Aleksandar Zlateski
Research Scientist - Facebook AI Research (FAIR)
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Title
Cited by
Cited by
Year
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
4692014
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
842015
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
522018
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
522016
Image segmentation by size-dependent single linkage clustering of a watershed basin graph
A Zlateski, HS Seung
arXiv preprint arXiv:1505.00249, 2015
482015
Binary and analog variation of synapses between cortical pyramidal neurons
S Dorkenwald, NL Turner, T Macrina, K Lee, R Lu, J Wu, AL Bodor, ...
BioRxiv, 2019
452019
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
412018
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
322014
Multiscale and multimodal reconstruction of cortical structure and function
NL Turner, T Macrina, JA Bae, R Yang, AM Wilson, C Schneider-Mizell, ...
Biorxiv, 2020
312020
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
312016
ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs
A Zlateski, K Lee, HS Seung
SC'16: Proceedings of the International Conference for High Performance …, 2016
292016
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
282020
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
222017
Oligodendrocyte precursor cells prune axons in the mouse neocortex
JA Buchanan, L Elabbady, F Collman, NL Jorstad, TE Bakken, C Ott, ...
BioRxiv, 2021
142021
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
142017
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
122019
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
122018
Scalable training of 3D convolutional networks on multi-and many-cores
A Zlateski, K Lee, HS Seung
Journal of Parallel and Distributed Computing 106, 195-204, 2017
112017
Pznet: efficient 3D convnet inference on manycore CPUs
S Popovych, D Buniatyan, A Zlateski, K Li, HS Seung
Science and Information Conference, 369-383, 2019
82019
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
62022
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