Zobrazeno 1 - 10
of 2 301
pro vyhledávání: '"Filipovich A"'
Optical training of large-scale Transformers and deep neural networks with direct feedback alignment
Autor:
Wang, Ziao, Müller, Kilian, Filipovich, Matthew, Launay, Julien, Ohana, Ruben, Pariente, Gustave, Mokaadi, Safa, Brossollet, Charles, Moreau, Fabien, Cappelli, Alessandro, Poli, Iacopo, Carron, Igor, Daudet, Laurent, Krzakala, Florent, Gigan, Sylvain
Modern machine learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly considered for inference but, to date, remain mostly limited to rela
Externí odkaz:
http://arxiv.org/abs/2409.12965
Autor:
Buckley, Andy, Corpe, Louie, Filipovich, Matthew, Gutschow, Christian, Rozinsky, Nick, Thor, Simon, Yeh, Yoran, Yellen, Jamie
Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, esp
Externí odkaz:
http://arxiv.org/abs/2312.15070
Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. Previous experimental demonstrations
Externí odkaz:
http://arxiv.org/abs/2310.03679
Publikováno v:
The Application of Clinical Genetics, Vol 2014, Iss default, Pp 55-66 (2014)
David Buchbinder,1 Diane J Nugent,1 Alexandra H Fillipovich2 1Division of Hematology, Children's Hospital of Orange County, Orange, CA, USA; 2Division of Immunology, Cincinnati Children's Hospital, Cincinnati, OH, USA Abstract: Wiskott–Aldrich synd
Externí odkaz:
https://doaj.org/article/e84a72bbbdf34c189636fd90949e830d
Alternatives to backpropagation have long been studied to better understand how biological brains may learn. Recently, they have also garnered interest as a way to train neural networks more efficiently. By relaxing constraints inherent to backpropag
Externí odkaz:
http://arxiv.org/abs/2210.14593
Autor:
Filipovich, Matthew J., Guo, Zhimu, Al-Qadasi, Mohammed, Marquez, Bicky A., Morison, Hugh D., Sorger, Volker J., Prucnal, Paul R., Shekhar, Sudip, Shastri, Bhavin J.
Publikováno v:
Optica 9, 1323-1332 (2022)
There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digital electronics. Here, we propose on-chip training of neural networks en
Externí odkaz:
http://arxiv.org/abs/2111.06862
Autor:
Hanebutte, Ulf, Baldwin, Andrew, Durakovic, Senad, Filipovich, Igor, Chien-Chun, Chou, Adamowicz, Damian, Chickles, Derek, Hawkes, David
This paper presents a methodology to separate the quantization process from the hardware-specific model compilation stage via a pre-quantized deep learning model description in standard ONNX format. Separating the quantization process from the model
Externí odkaz:
http://arxiv.org/abs/2110.01730
Autor:
Chiodi, A., Báez, W., Tassi, F., Bustos, E., Filipovich, R., Murray, J., Rizzo, A.L., Vaselli, O., Giordano, G., Viramonte, J.G.
Publikováno v:
In Journal of Volcanology and Geothermal Research June 2024 450
PyCharge is a computational electrodynamics Python simulator that can calculate the electromagnetic fields and potentials generated by moving point charges and can self-consistently simulate dipoles modeled as Lorentz oscillators. To calculate the to
Externí odkaz:
http://arxiv.org/abs/2107.12437
We present a computational methodology to directly calculate and visualize the directional components of the Coulomb, radiation, and total electromagnetic fields, as well as the scalar and vector potentials, generated by moving point charges in arbit
Externí odkaz:
http://arxiv.org/abs/2010.01558