Zobrazeno 1 - 10 of 38 pro vyhledávání: '"Martin M. Stein"'
1
Akademický článek
Exploring the effects of omitted variable bias in physics education research
Autor: Cole Walsh, Martin M. Stein, Ryan Tapping, Emily M. Smith, N. G. Holmes
Publikováno v: Physical Review Physics Education Research, Vol 17, Iss 1, p 010119 (2021)
Omitted variable bias occurs in most statistical models. Whenever a confounding variable that is correlated with both dependent and independent variables is omitted from a statistical model, estimated effects of included variables are likely to be bi
Externí odkaz: https://doaj.org/article/3553fa02740a49ce869323f6437a550c
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2
Akademický článek
How expectations of confirmation influence students’ experimentation decisions in introductory labs
Autor: Emily M. Smith, Martin M. Stein, N. G. Holmes
Publikováno v: Physical Review Physics Education Research, Vol 16, Iss 1, p 010113 (2020)
Many instructional physics labs are shifting to teach experimentation skills, rather than to demonstrate or confirm canonical physics phenomena. Our previous work found that many students engage in questionable research practices in attempts to confi
Externí odkaz: https://doaj.org/article/2712de27b646430cb1e0330f49e9a8f9
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3
Akademický článek
Direct Measurement of the Impact of Teaching Experimentation in Physics Labs
Autor: Emily M. Smith, Martin M. Stein, Cole Walsh, N. G. Holmes
Publikováno v: Physical Review X, Vol 10, Iss 1, p 011029 (2020)
While there have been many calls to improve the quality of instructional physics labs, there exists little research on the effectiveness of lab instruction. This study provides a direct comparison between labs that have goals to reinforce physics con
Externí odkaz: https://doaj.org/article/68bf366d2603460098423bbd653f97a1
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5
Deep physical neural networks based on ultrafast nonlinear optics
Autor: Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Tianyu Wang, Darren T. Schachter, Zoey Hu, Peter L. McMahon
Publikováno v: AI and Optical Data Sciences IV.
We experimentally demonstrate multilayer neural networks using ultrafast nonlinear optics, to perform audio and image classification. The proposed framework for constructing and training neural networks is general and applicable to other complex non-
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::440e584b1c2bc0bedfe1f64cbef3b46f
https://doi.org/10.1117/12.2648103
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7
Deep physical neural networks trained with backpropagation
Autor: Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Tianyu Wang, Darren T. Schachter, Zoey Hu, Peter L. McMahon
Publikováno v: Nature
Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability1. Deep-learning accelerators2–9 aim to perform deep learning energy-efficiently, usually target
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c1772ee7c92b096487064e4b035e7b6
http://europepmc.org/articles/PMC8791835
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9
Image sensing with multilayer, nonlinear optical neural networks
Autor: Tianyu Wang, Mandar M. Sohoni, Logan G. Wright, Martin M. Stein, Shi-Yuan Ma, Tatsuhiro Onodera, Maxwell G. Anderson, Peter L. McMahon
Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object's position, is performed by computational analysis of a digitized image. An emergi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48a18573dd77d0958936b8d10245d5a9
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10
Image Sensing with Nonlinear Optical Neural Networks
Autor: Mandar M. Sohoni, Tianyu Wang, Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Shi-Yuan Ma, Maxwell Anderson, Peter L. McMahon
Publikováno v: Optica Advanced Photonics Congress 2022.
We demonstrate a multilayer optical-neural-network image encoder realized using optoelectronic, optical-to-optical nonlinear activations. We show that nonlinear preprocessing in the optical domain can enable machine-vision systems that operate faster
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d64182acd123e3d35b32a84c31046d22
https://doi.org/10.1364/bgppm.2022.jtu2a.43
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