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检索条件"机构=Biomedical Data Science and Machine Learning Group"
286 条 记 录,以下是201-210 订阅
排序:
Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes
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JMIR Research Protocols 2018年 第7期7卷 e10961页
作者: Suominen, Hanna Kelly, Liadh Goeuriot, Lorraine Research School of Computer Science College of Engineering and Computer Science The Australian National University Canberra ACT Australia Machine Learning Research Group Data61 Commonwealth Scientific and Industrial Research Organisation Canberra ACT Australia Faculty of Science and Technology University of Canberra Canberra ACT Australia Department of Future Technologies Faculty of Science and Engineering University of Turku Turku Finland Department of Computer Science Maynooth University Maynooth Co Kildare Ireland Grenoble Informatics Laboratory Université Grenoble Alpes Grenoble France
Background: The eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to provide researchers working on health text analytics with annual workshops, shared development chall... 详细信息
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Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design
arXiv
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arXiv 2024年
作者: Weinreich, Jan Karandashev, Konstantin Arrieta, Daniel Jose Arismendi Hermansson, Kersti Anatole von Lilienfeld, O. LausanneCH-1015 Switzerland University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Department of Chemistry-Ångström Laboratory Uppsala University Box 538 Uppsala75121 Sweden Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany
We present high-quality reference data for two fundamentally important groups of molecular properties related to a compound's utility as a lithium battery electrolyte. The first one is energy changes associated wi... 详细信息
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machine learning Force Fields
arXiv
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arXiv 2020年
作者: Unke, Oliver T. Chmiela, Stefan Sauceda, Huziel E. Gastegger, Michael Poltavsky, Igor Schütt, Kristof T. Tkatchenko, Alexandre Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Berlin10587 Germany Technische Universität Berlin Berlin10623 Germany BASLEARN BASF-TU joint Lab Technische Universität Berlin Berlin10587 Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany
In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One o... 详细信息
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Towards DMC accuracy across chemical space with scalable ∆-QML
arXiv
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arXiv 2022年
作者: Huang, Bing von Lilienfeld, O. Anatole Krogel, Jaron T. Benali, Anouar University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences Division Argonne National Laboratory ArgonneIL60439 United States
In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-bo... 详细信息
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Traffic4cast at NeurIPS 2021 – Temporal and Spatial Few-Shot Transfer learning in Gridded Geo-Spatial Processes
arXiv
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arXiv 2022年
作者: Eichenberger, Christian Neun, Moritz Martin, Henry Herruzo, Pedro Spanring, Markus Lu, Yichao Choi, Sungbin Konyakhin, Vsevolod Lukashina, Nina Shpilman, Aleksei Wiedemann, Nina Raubal, Martin Wang, Bo Vu, Hai L. Mohajerpoor, Reza Cai, Chen Kim, Inhi Hermes, Luca Melnik, Andrew Velioglu, Riza Vieth, Markus Schilling, Malte Bojesomo, Alabi Al Marzouqi, Hasan Liatsis, Panos Santokhi, Jay Hillier, Dylan Yang, Yiming Sarwar, Joned Jordan, Anna Hewage, Emil Jonietz, David Tang, Fei Gruca, Aleksandra Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland Layer 6 AI Toronto Canada ITMO University Saint Petersburg Russia JetBrains Research Saint Petersburg Russia HSE University Saint Petersburg Russia Institute of Transport Studies Monash University ClaytonVIC Australia CSIRO’s Data61 Eveleigh Australia Institute Civil and Environmental Engineering Department Kongju National University Korea Republic of Machine Learning & Neuroinformatics Group Bielefeld University Germany Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Alchera Data Technologies Ltd Cambridge United Kingdom HERE Technologies Zurich Switzerland Silesian University of Technology Gliwice Poland Machine Learning Institute Johannes Kepler University Linz Austria
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space ... 详细信息
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Pushing the boundaries of asteroseismic individual frequency modelling: Unveiling two evolved very low-metallicity red giants
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Astronomy and Astrophysics 2025年 697卷
作者: Larsen, J.R. Rørsted, J.L. Aguirre Børsen-Koch, V. Lundkvist, M.S. Christensen-Dalsgaard, J. Winther, M.L. Stokholm, A. Li, Y. Slumstrup, D. Kjeldsen, H. Corsaro, E. Benomar, O. Dhanpal, S. Weiss, A. Mosser, B. Hekker, S. Stello, D. Korn, A.J. Jendreieck, A. Elsworth, Y. Handberg, R. Kallinger, T. Jiang, C. Ruchti, G. Stellar Astrophysics Centre (SAC) Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C. 8000 Denmark Aarhus Astronomy Data Centre (AADC) Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C. 8000 Denmark DARK Niels Bohr Institute University of Copenhagen Jagtvej 128 18 Copenhagen 2200 Denmark School of Physics and Astronomy University of Birmingham Edgbaston B15 2TT United Kingdom Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive Honolulu 96822 HI United States GRANTECAN Cuesta de San José s/n La Palma Breña Baja E-38712 Spain Instituto de Astrofísica de Canarias Tenerife La Laguna E-38205 Spain European Southern Observatory Alonso de Cordova 3107 Vitacura Chile Aarhus Space Centre (SpaCe) Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C. 8000 Denmark INAF Osservatorio Astrofisico di Catania Via S. Sofia 78 Catania 95123 Italy Department of Astronomical Science School of Physical Sciences SOKENDAI 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Solar Science Observatory National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Scientific Machine Learning group Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus Didcot OX11 0QX United Kingdom Max-Planck-Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching 85748 Germany LIRA Observatoire de Paris Université PSL CNRS Sorbonne Université Université Paris-Cité Meudon 92195 France Heidelberger Institut für Theoretische Studien Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Center for Astronomy (ZAH/LSW) Heidelberg University Königstuhl 12 Heidelberg 69117 Germany School of Physics University of New South Wales Sydney 2052 NSW Australia Sydney Institute for Astronomy (SIfA) School of Physics University of Sydney Sydney 2006 NSW Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimension
Context. Metal-poor stars play a crucial role in understanding the nature and evolution of the first stellar generation in the Galaxy. Previously, asteroseismic characterisation of red-giant stars has relied on constr... 详细信息
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Improving Generative Model-based Unfolding with Schrödinger Bridges
arXiv
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arXiv 2023年
作者: Diefenbacher, Sascha Liu, Guan-Horng Mikuni, Vinicius Nachman, Benjamin Nie, Weili Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology AtlantaGA30332 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Machine Learning Research Group NVIDIA Research United States
machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area: one based on discriminative models and one ba... 详细信息
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Towards collaborative data science in mental health research: The ECNP neuroimaging network accessible data repository
Neuroscience Applied
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Neuroscience Applied 2025年 4卷
作者: Khuntia, Adyasha Buciuman, Madalina-Octavia Fanning, John Stolicyn, Aleks Vetter, Clara Armio, Reetta-Liina From, Tiina Goffi, Federica Hahn, Lisa Kaufmann, Tobias Laurikainen, Heikki Maggioni, Eleonora Martinez-Zalacain, Ignacio Ruef, Anne Dong, Mark Sen Schwarz, Emanuel Squarcina, Letizia Andreassen, Ole Bellani, Marcella Brambilla, Paolo Haren, Neeltje van Hietala, Jarmo Lawrie, Stephen M. Soriano-Mas, Carles Whalley, Heather Taquet, Maxime Meisenzahl, Eva Falkai, Peter Wiegand, Ariane Koutsouleris, Nikolaos Department of Psychiatry and Psychotherapy University Hospital Ludwig-Maximilian University Munich Germany International Max-Planck School for Translational Psychiatry Germany Max-Planck Institute of Psychiatry Munich Munich Germany Max Planck Institute of Psychiatry Munich Germany German Center for Mental Health Partner Site Munich Augsburg Germany Division of Psychiatry Centre for Clinical Brain Sciences University of Edinburgh Scotland Edinburgh United Kingdom Munich Center of Machine Learning Munich Germany Helmholtz Association - Munich School for Data Science Munich Germany Department of Psychiatry University of Turku Turku Finland Turku PET Centre University of Turku Turku Finland Turku University Hospital Turku Finland Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Neurosciences and Mental Health Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico University of Milan Milan Italy NORMENT Centre Division of Mental Health and Addiction Oslo University Hospital Oslo Norway Department of Psychiatry and Psychotherapy University of Tübingen Germany German Center for Mental Health Partner Site Tübingen Germany Bellvitge Biomedical Research Institute-IDIBELL Psychiatry Department Bellvitge University Hospital CIBERSAM and Department of Psychobiology and Methodology of Health Sciences Universitat Autònoma de Barcelona Spain Department of Radiology Bellvitge University Hospital Barcelona Spain Hector Institute for Artificial Intelligence in Psychiatry Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany Department of Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany German Center for Mental Health Partner Site Mannheim-Heidelberg-Ulm Germany Department of Pathophysiology and Transplantation University of Milan Milan Italy Section of Psychiatry Dept of Neuroscience
The current biologically uninformed psychiatric taxonomy complicates optimal diagnosis and treatment. Neuroimaging-based machine learning methods hold promise for tackling these issues, but large-scale, representative... 详细信息
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Accurate global machine learning force fields for molecules with hundreds of atoms
arXiv
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arXiv 2022年
作者: Chmiela, Stefan Vassilev-Galindo, Valentin Unke, Oliver T. Kabylda, Adil Sauceda, Huziel E. Tkatchenko, Alexandre Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data – BIFOLD Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg Google Research Brain Team Berlin Germany Departamento de Materia Condensada Instituto de Física Universidad Nacional Autónoma de México Cd. de MéxicoC.P. 04510 Mexico BASLEARN - TU Berlin BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of
Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms due a considerable growth of the mod... 详细信息
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Occam's razor for AI: Coarse-graining Hammett Inspired Product Ansatz in Chemical Space
arXiv
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arXiv 2023年
作者: Bragato, Marco Von Rudorff, Guido Falk Von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 1416 WienAT1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany
data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality exp... 详细信息
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