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pro vyhledávání: '"Bates, Daniel"'
Autor:
Bates, Daniel J., Breiding, Paul, Chen, Tianran, Hauenstein, Jonathan D., Leykin, Anton, Sottile, Frank
Numerical nonlinear algebra is a computational paradigm that uses numerical analysis to study polynomial equations. Its origins were methods to solve systems of polynomial equations based on the classical theorem of B\'ezout. This was decisively link
Externí odkaz:
http://arxiv.org/abs/2302.08585
Muntjac is an open-source collection of components which can be used to build a multicore, Linux-capable system-on-chip. This includes a 64-bit RISC-V core, a cache subsystem, and TileLink interconnect allowing cache-coherent multicore configurations
Externí odkaz:
http://arxiv.org/abs/2206.06769
We present a novel method for using Neural Networks (NNs) for finding solutions to a class of Partial Differential Equations (PDEs). Our method builds on recent advances in Neural Radiance Field research (NeRFs) and allows for a NN to converge to a P
Externí odkaz:
http://arxiv.org/abs/2205.08978
Publikováno v:
Military Operations Research, 2023 Jan 01. 28(3), 31-52.
Externí odkaz:
https://www.jstor.org/stable/27254914
Deep Graph Neural Networks (GNNs) show promising performance on a range of graph tasks, yet at present are costly to run and lack many of the optimisations applied to DNNs. We show, for the first time, how to systematically quantise GNNs with minimal
Externí odkaz:
http://arxiv.org/abs/2009.09232
Autor:
Shumailov, Ilia, Zhao, Yiren, Bates, Daniel, Papernot, Nicolas, Mullins, Robert, Anderson, Ross
The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While this enabled us to train large-scale neural networks in datacenters and deploy them on edge devices, the focus so far
Externí odkaz:
http://arxiv.org/abs/2006.03463