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2020 – today
- 2021
[i18]Robin Winter, Frank Noé, Djork-Arné Clevert:
Auto-Encoding Molecular Conformations. CoRR abs/2101.01618 (2021)- [i17]Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert:
Parameterized Hypercomplex Graph Neural Networks for Graph Classification. CoRR abs/2103.16584 (2021) - 2020
- [j14]Robin Winter
, Joren Retel, Frank Noé, Djork-Arné Clevert, Andreas Steffen:
grünifai: interactive multiparameter optimization of molecules in a continuous vector space. Bioinform. 36(13): 4093-4094 (2020) - [j13]Hao Wu, Frank Noé:
Variational Approach for Learning Markov Processes from Time Series Data. J. Nonlinear Sci. 30(1): 23-66 (2020) - [c11]Jonas Köhler, Leon Klein, Frank Noé:
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities. ICML 2020: 5361-5370 - [c10]Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu:
Deep learning Markov and Koopman models with physical constraints. MSML 2020: 451-475 - [c9]Hao Wu, Jonas Köhler, Frank Noé:
Stochastic Normalizing Flows. NeurIPS 2020 - [i16]Hao Wu, Jonas Köhler, Frank Noé:
Stochastic Normalizing Flows. CoRR abs/2002.06707 (2020) - [i15]Jonas Köhler, Leon Klein, Frank Noé:
Equivariant Flows: exact likelihood generative learning for symmetric densities. CoRR abs/2006.02425 (2020) - [i14]Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé:
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR abs/2008.08461 (2020) - [i13]Zeno Schätzle, Jan Hermann, Frank Noé:
Convergence to the fixed-node limit in deep variational Monte Carlo. CoRR abs/2010.05316 (2020) - [i12]Andreas Krämer, Jonas Köhler, Frank Noé:
Training Neural Networks with Property-Preserving Parameter Perturbations. CoRR abs/2010.07033 (2020) - [i11]Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noé, Toni Giorgino, Gianni De Fabritiis:
TorchMD: A deep learning framework for molecular simulations. CoRR abs/2012.12106 (2020)
2010 – 2019
- 2019
- [j12]Moritz Hoffmann, Christoph Fröhner, Frank Noé:
ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics. PLoS Comput. Biol. 15(2) (2019) - [c8]John R. Ossyra, Ada Sedova, Arnold N. Tharrington, Frank Noé, Cecilia Clementi, Jeremy C. Smith:
Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer. ISC Workshops 2019: 397-417 - [i10]Jan Hermann, Zeno Schätzle, Frank Noé:
Deep neural network solution of the electronic Schrödinger equation. CoRR abs/1909.08423 (2019) - [i9]Jonas Köhler, Leon Klein, Frank Noé:
Equivariant Flows: sampling configurations for multi-body systems with symmetric energies. CoRR abs/1910.00753 (2019) - [i8]Moritz Hoffmann, Frank Noé:
Generating valid Euclidean distance matrices. CoRR abs/1910.03131 (2019) - [i7]Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi:
Machine learning for molecular simulation. CoRR abs/1911.02792 (2019) - 2018
- [j11]Péter Koltai, Hao Wu, Frank Noé, Christof Schütte:
Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics. Comput. 6(1): 22 (2018) - [j10]Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis G. Kevrekidis, Christof Schütte, Frank Noé:
Data-Driven Model Reduction and Transfer Operator Approximation. J. Nonlinear Sci. 28(3): 985-1010 (2018) - [c7]Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé:
Deep Generative Markov State Models. NeurIPS 2018: 3979-3988 - [i6]Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé:
Deep Generative Markov State Models. CoRR abs/1805.07601 (2018) - [i5]Frank Noé, Hao Wu:
Boltzmann Generators - Sampling Equilibrium States of Many-Body Systems with Deep Learning. CoRR abs/1812.01729 (2018) - [i4]Jiang Wang, Christoph Wehmeyer, Frank Noé, Cecilia Clementi:
Machine Learning of coarse-grained Molecular Dynamics Force Fields. CoRR abs/1812.01736 (2018) - [i3]Frank Noé:
Machine Learning for Molecular Dynamics on Long Timescales. CoRR abs/1812.07669 (2018) - 2017
- [i2]Christoph Wehmeyer, Frank Noé:
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. CoRR abs/1710.11239 (2017) - 2016
- [c6]Hao Wu, Frank Noé:
Spectral Learning of Dynamic Systems from Nonequilibrium Data. NIPS 2016: 4179-4187 - [i1]Hao Wu, Frank Noé:
Spectral learning of dynamic systems from nonequilibrium data. CoRR abs/1609.00932 (2016) - 2015
- [j9]Alexander Ullrich, Mathias A. Böhme, Johannes Schöneberg, Harald Depner, Stephan J. Sigrist, Frank Noé:
Dynamical Organization of Syntaxin-1A at the Presynaptic Active Zone. PLoS Comput. Biol. 11(9) (2015) - 2014
- [j8]Hao Wu, Frank Noé:
Optimal Estimation of Free Energies and Stationary Densities from Multiple Biased Simulations. Multiscale Model. Simul. 12(1): 25-54 (2014) - 2013
- [j7]Frank Noé, Feliks Nüske:
A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. Multiscale Model. Simul. 11(2): 635-655 (2013) - [j6]York Posor, Marielle Eichhorn-Gruenig, Dmytro Puchkov, Johannes Schöneberg, Alexander Ullrich, André Lampe, Rainer Müller, Sirus Zarbakhsh, Federico Gulluni, Emilio Hirsch, Michael Krauss, Carsten Schultz, Jan Schmoranzer, Frank Noé, Volker Haucke:
Spatiotemporal control of endocytosis by phosphatidylinositol-3, 4-bisphosphate. Nat. 499(7457): 233-237 (2013) - 2011
- [j5]Jan-Hendrik Prinz, Martin Held, Jeremy C. Smith, Frank Noé:
Efficient Computation, Sensitivity, and Error Analysis of Committor Probabilities for Complex Dynamical Processes. Multiscale Model. Simul. 9(2): 545-567 (2011) - [c5]Hao Wu, Frank Noé:
A flat Dirichlet process switching model for Bayesian estimation of hybrid systems. ICCS 2011: 1393-1402 - 2010
- [j4]Marco Sarich, Frank Noé, Christof Schütte:
On the Approximation Quality of Markov State Models. Multiscale Model. Simul. 8(4): 1154-1177 (2010) - [j3]Hao Wu, Frank Noé:
Probability Distance Based Compression of Hidden Markov Models. Multiscale Model. Simul. 8(5): 1838-1861 (2010) - [c4]Hao Wu, Frank Noé:
Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields. ICCS 2010: 1665-1673
2000 – 2009
- 2007
- [j2]Lin Wang, Minghu Jiang, Yinghua Lu, Minfu Sun, Frank Noé:
A Comparative Study of Clustering Methods for Molecular Data. Int. J. Neural Syst. 17(6): 447-458 (2007) - [c3]Frank Noé, Marcus Oswald, Gerhard Reinelt:
Optimizing in Graphs with Expensive Computation of Edge Weights. OR 2007: 435-440 - 2006
- [j1]Frank Noé, Marcus Oswald, Gerhard Reinelt, Stefan Fischer, Jeremy C. Smith:
Computing Best Transition Pathways in High-Dimensional Dynamical Systems: Application to the AlphaL \leftrightharpoons Beta \leftrightharpoons AlphaR Transitions in Octaalanine. Multiscale Model. Simul. 5(2): 393-419 (2006) - [c2]Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith:
Clustering Analysis of Competitive Learning Network for Molecular Data. ISNN (1) 2006: 1244-1249 - [c1]Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith:
Self-Organizing Map Clustering Analysis for Molecular Data. ISNN (1) 2006: 1250-1255
Coauthor Index
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