Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Coquelin, Daniel"'
The gradients used to train neural networks are typically computed using backpropagation. While an efficient way to obtain exact gradients, backpropagation is computationally expensive, hinders parallelization, and is biologically implausible. Forwar
Externí odkaz:
http://arxiv.org/abs/2410.17764
Autor:
Coquelin, Daniel, Flügel, Katherina, Weiel, Marie, Kiefer, Nicholas, Öz, Muhammed, Debus, Charlotte, Streit, Achim, Götz, Markus
Communication bottlenecks severely hinder the scalability of distributed neural network training, particularly in high-performance computing (HPC) environments. We introduce AB-training, a novel data-parallel method that leverages low-rank representa
Externí odkaz:
http://arxiv.org/abs/2405.01067
Autor:
Coquelin, Daniel, Flügel, Katharina, Weiel, Marie, Kiefer, Nicholas, Debus, Charlotte, Streit, Achim, Götz, Markus
This study explores the learning dynamics of neural networks by analyzing the singular value decomposition (SVD) of their weights throughout training. Our investigation reveals that an orthogonal basis within each multidimensional weight's SVD repres
Externí odkaz:
http://arxiv.org/abs/2401.08505
Autor:
Flügel, Katharina, Coquelin, Daniel, Weiel, Marie, Debus, Charlotte, Streit, Achim, Götz, Markus
Backpropagation has long been criticized for being biologically implausible, relying on concepts that are not viable in natural learning processes. This paper proposes an alternative approach to solve two core issues, i.e., weight transport and updat
Externí odkaz:
http://arxiv.org/abs/2304.13372
Autor:
Taubert, Oskar, Weiel, Marie, Coquelin, Daniel, Farshian, Anis, Debus, Charlotte, Schug, Alexander, Streit, Achim, Götz, Markus
We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, Propulate omits the synchronization after each generation as done i
Externí odkaz:
http://arxiv.org/abs/2301.08713
Autor:
Coquelin, Daniel, Rasti, Behnood, Götz, Markus, Ghamisi, Pedram, Gloaguen, Richard, Streit, Achim
As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use
Externí odkaz:
http://arxiv.org/abs/2204.06979
Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)
Autor:
Coquelin, Daniel, Debus, Charlotte, Götz, Markus, von der Lehr, Fabrice, Kahn, James, Siggel, Martin, Streit, Achim
Publikováno v:
J Big Data 9, 14 (2022)
With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) to utilize large-scale
Externí odkaz:
http://arxiv.org/abs/2104.05588
Autor:
Götz, Markus, Coquelin, Daniel, Debus, Charlotte, Krajsek, Kai, Comito, Claudia, Knechtges, Philipp, Hagemeier, Björn, Tarnawa, Michael, Hanselmann, Simon, Siggel, Martin, Basermann, Achim, Streit, Achim
To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are li
Externí odkaz:
http://arxiv.org/abs/2007.13552
Akademický článek
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Autor:
Comito, Claudia, Hagemeier, Björn, Tarnawa, Michael, Krajsek, Kai, Götz, Markus, Coquelin, Daniel, Gutiérrez Hermosillo Muriedas, Juan Pedro, Knechtges, Philipp, Rüttgers, Alexander
When it comes to enhancing exploitation of massive data, machine learning and AI methods are very much at the forefront of our awareness. Much less so is the need for, and complexity of, applying these techniques efficiently across memory-distributed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02dee0e2642294006b0479cfca835c8d