Zobrazeno 1 - 10
of 162
pro vyhledávání: '"A. Zlateski"'
Autor:
Mitenkov, George, Kabiljo, Igor, Li, Zekun, Spiegelman, Alexander, Vusirikala, Satyanarayana, Xiang, Zhuolun, Zlateski, Aleksandar, Lopes, Nuno P., Gelashvili, Rati
One of the main bottlenecks of blockchains is smart contract execution. To increase throughput, modern blockchains try to execute transactions in parallel. Unfortunately, however, common blockchain use cases introduce read-write conflicts between tra
Externí odkaz:
http://arxiv.org/abs/2405.06117
Advanced compiler technology is crucial for enabling machine learning applications to run on novel hardware, but traditional compilers fail to deliver performance, popular auto-tuners have long search times and expert-optimized libraries introduce un
Externí odkaz:
http://arxiv.org/abs/2309.01825
We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack enables us to compile entire neural networks and generate code targeting
Externí odkaz:
http://arxiv.org/abs/2205.00618
Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions. Here we describe a distributed algorithm capable of handling a tremendous number of supervoxels. The algorithm works recursively, the reg
Externí odkaz:
http://arxiv.org/abs/2106.10795
Fast convolutions via transforms, either Winograd or FFT, had emerged as a preferred way of performing the computation of convolutional layers, as it greatly reduces the number of required operations. Recent work shows that, for many layer structures
Externí odkaz:
http://arxiv.org/abs/1912.02165
Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks. Many tasks within biomedical analysis domain involve analyzing volumetric (3D) data acquired by CT, MRI and Microscopy acquisition method
Externí odkaz:
http://arxiv.org/abs/1903.07525
Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks (ConvNet) on various hardware platforms because it requires fewer floating point operations than FFT-based or direct convolution
Externí odkaz:
http://arxiv.org/abs/1809.07851
Sliding window convolutional networks (ConvNets) have become a popular approach to computer vision problems such as image segmentation, and object detection and localization. Here we consider the problem of inference, the application of a previously
Externí odkaz:
http://arxiv.org/abs/1606.05688
Autor:
Sven Dorkenwald, Nicholas L Turner, Thomas Macrina, Kisuk Lee, Ran Lu, Jingpeng Wu, Agnes L Bodor, Adam A Bleckert, Derrick Brittain, Nico Kemnitz, William M Silversmith, Dodam Ih, Jonathan Zung, Aleksandar Zlateski, Ignacio Tartavull, Szi-Chieh Yu, Sergiy Popovych, William Wong, Manuel Castro, Chris S Jordan, Alyssa M Wilson, Emmanouil Froudarakis, JoAnn Buchanan, Marc M Takeno, Russel Torres, Gayathri Mahalingam, Forrest Collman, Casey M Schneider-Mizell, Daniel J Bumbarger, Yang Li, Lynne Becker, Shelby Suckow, Jacob Reimer, Andreas S Tolias, Nuno Macarico da Costa, R Clay Reid, H Sebastian Seung
Publikováno v:
eLife, Vol 11 (2022)
Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), whi
Externí odkaz:
https://doaj.org/article/27b1354692bd4be48848ffb48a230736
Autor:
William Silversmith, Aleksandar Zlateski, J. Alexander Bae, Ignacio Tartavull, Nico Kemnitz, Jingpeng Wu, H. Sebastian Seung
Publikováno v:
Frontiers in Neural Circuits, Vol 16 (2022)
Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarc
Externí odkaz:
https://doaj.org/article/67106399db1f4c819fa84e17870aa527