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Kim A. Nicoli
Kim A. Nicoli
PhD, Technische Universität
Email verificata su tu-berlin.de - Home page
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Citata da
Citata da
Anno
SchNetPack: A deep learning toolbox for atomistic systems
KT Schütt, P Kessel, M Gastegger, KA Nicoli, A Tkatchenko, KR Müller
Journal of chemical theory and computation 15 (1), 448-455, 2018
2512018
Asymptotically unbiased estimation of physical observables with neural samplers
KA Nicoli, S Nakajima, N Strodthoff, W Samek, KR Müller, P Kessel
Phys. Rev. E 101 (2), 023304, 2020
632020
Estimation of thermodynamic observables in lattice field theories with deep generative models
KA Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, P Kessel, ...
Physical Review Letters 126 (3), 032001, 2021
522021
Modern applications of machine learning in quantum sciences
A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ...
arXiv preprint arXiv:2204.04198, 2022
82022
Comment on" Solving Statistical Mechanics Using VANs": Introducing saVANt-VANs Enhanced by Importance and MCMC Sampling
KA Nicoli, P Kessel, N Strodthoff, W Samek, KR Müller, S Nakajima
arXiv preprint arXiv:1903.11048, 2019
42019
Gradients should stay on path: better estimators of the reverse-and forward KL divergence for normalizing flows
L Vaitl, KA Nicoli, S Nakajima, P Kessel
Machine Learning: Science and Technology 3 (4), 045006, 2022
22022
Path-gradient estimators for continuous normalizing flows
L Vaitl, KA Nicoli, S Nakajima, P Kessel
International Conference on Machine Learning, 21945-21959, 2022
22022
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
KA Nicoli, C Anders, L Funcke, T Hartung, K Jansen, P Kessel, ...
The 38th International Symposium on Lattice Field Theory, LATTICE2021, 2022
12022
Analysis of Atomistic Representations Using Weighted Skip-Connections
KA Nicoli, P Kessel, M Gastegger, KT Schütt
arXiv preprint arXiv:1810.09751, 2018
2018
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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