Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Fischetti, Giulia"'
Publikováno v:
Phys. Rev. E 103, 060401 (2021)
We study the recognition capabilities of the Hopfield model with auxiliary hidden layers, which emerge naturally upon a Hubbard-Stratonovich transformation. We show that the recognition capabilities of such a model at zero-temperature outperform thos
Externí odkaz:
http://arxiv.org/abs/2101.05247
Autor:
Schmid, Nicolas, Bruderer, Simon, Fischetti, Giulia, Paruzzo, Federico, Toscano, Giuseppe, Graf, Dominik, Fey, Michael, Ziebart, Volker, Henrici, Andreas, Grabner, Helmut, Wegner, Jan Dirk, Sigel, Roland K.O., Heitmann, Björn, Wilhelm, Dirk
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e64e36c83c512ff46ab13cec934a526d
https://hdl.handle.net/11475/27429
https://hdl.handle.net/11475/27429
Autor:
Fischetti, Giulia, Schmid, Nicolas, Bruderer, Simon, Caldarelli, Guido, Scarso, Alessandro, Henrici, Andreas, Wilhelm, Dirk
The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1060c7b1d650237b453ec94e483defa2
Autor:
Schmid, Nicolas, Bruderer, Simon, Fischetti, Giulia, Paruzzo, Federico, Toscano, Giuseppe, Graf, Dominik, Fey, Michael, Henrici, Andreas, Grabner, Helmut, Wegner, Jan Dirk, Sigel, Roland K. O., Heitmann, Björn, Wilhelm, Dirk
We introduce a deep learning-based deconvolution approach for 1H NMR spectra, developed by leveraging concepts from the field of physics informed-learning, intelligent labeling, and tailored high dynamic range (HDR) spectral preprocessing. Since auto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::527fe80490b3cf15579e2180cc5bd787
Autor:
Fischetti, Giulia, Schmid, Nicolas, Bruderer, Simon, Paruzzo, Federico, Toscano, Giuseppe, Graf, Dominik, Fey, Michael, Henrici, Andreas, Scarso, Alessandro, Caldarelli, Guido, Heitmann, Björn, Wilhelm, Dirk
The identification and characterization of signal peaks in NMR spectra is a crucial yet time-consuming and error-prone stage in the determination of complex chemical compounds. The introduction of automation in the NMR analysis can ease the workflow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::996efddafbc6d5d28559b3c0970493fd
Autor:
Fischetti G; Dipartimento di Scienze Molecolari e Nanosistemi, Ca' Foscari Università di Venezia, Venice, Italy., Schmid N; Zürcher Hochschule für Angewandte Wissenschaften (ZHAW), Zurich, Switzerland.; Institute for Computational Science, Universität Zürich (UZH), Zurich, Switzerland., Bruderer S; Bruker Schweiz AG, Fällanden, Switzerland., Caldarelli G; Dipartimento di Scienze Molecolari e Nanosistemi, Ca' Foscari Università di Venezia, Venice, Italy., Scarso A; Dipartimento di Scienze Molecolari e Nanosistemi, Ca' Foscari Università di Venezia, Venice, Italy., Henrici A; Zürcher Hochschule für Angewandte Wissenschaften (ZHAW), Zurich, Switzerland., Wilhelm D; Zürcher Hochschule für Angewandte Wissenschaften (ZHAW), Zurich, Switzerland.
Publikováno v:
Frontiers in artificial intelligence [Front Artif Intell] 2023 Jan 11; Vol. 5, pp. 1116416. Date of Electronic Publication: 2023 Jan 11 (Print Publication: 2022).
