- Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes
2022/07/16 by Mikhail Tsitsvero, Tsitsvero, Mikhail, Jin, Mingoo +1 · 1 citation
Computer Science · Materials Science · #60-08 #60G15 #68-04 #68T99 #92E99 #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science
- Predicting the orientation of adsorbed proteins steered with electric fields using a simple electrostatic model
2021/12/10 by Sergio A. Urzúa, Perla Y. Sauceda-Oloño, Urzúa, Sergio A. +5 · 2 citations
Engineering · Materials Science · #92E99 #FOS: Physical sciences #J.2 #Microfluidic and Bio-sensing Technologies #Microfluidic and Capillary Electrophoresis Applications #Polymer Surface Interaction Studies #Soft Condensed Matter (cond-mat.soft)
- Tree balance indices: a comprehensive survey
2021/09/25 by Mareike Fischer, Fischer, Mareike, Herbst, Lina +6 · 5 citations
Agricultural and Biological Sciences · Earth and Planetary Sciences · Environmental Science · #05C05 #05C35 #05C90 #92E99 #Combinatorics (math.CO) #Ecology and Vegetation Dynamics Studies #Evolution and Paleontology Studies #FOS: Biological sciences #FOS: Mathematics #G.2.1 #G.2.2 #G.4 #Plant and animal studies #Populations and Evolution (q-bio.PE)
- Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost\n Functions
2020/06/25 by Vladimir Golkov, Alexander Becker, Golkov, Vladimir +16 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #62F07 (Secondary) #68T07 (Primary) 62H30 #68T10 #92E99 #Biomolecules (q-bio.BM) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #G.3 #Genetics, Bioinformatics, and Biomedical Research #I.2.1 #I.2.6 #I.5.1 #J.3 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Machine Learning in Materials Science #Quantitative Methods (q-bio.QM)
- Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
2020/02/17 by Andrea Gayon-Lombardo, Lukas Mosser, Gayon-Lombardo, Andrea +6 · 4 citations
Computer Science · Physics and Astronomy · #68T10 #68T45 #82D30 #92E99 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #I.4 #I.4.5 #J.2 #Machine Learning and ELM #Model Reduction and Neural Networks #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)
- Density functional theory and optimal transportation with Coulomb cost
2011/04/04 by Cotar, Codina, Friesecke, Gero, Klüppelberg, Claudia · 5 citations
#35Q40 #49S05 #65K99 #81V55 #82B05 #82C70 #92E99 #Analysis of PDEs (math.AP) #FOS: Mathematics #FOS: Physical sciences #Mathematical Physics (math-ph)