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2020 – today
- 2024
[j22]Shuxin Zheng
, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu:
Predicting equilibrium distributions for molecular systems with deep learning. Nat. Mac. Intell. 6(5): 558-567 (2024)- [j21]Patrick Bryant, Frank Noé:
Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile. PLoS Comput. Biol. 20(7): 1012253 (2024) - [j20]Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola:
Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. 2024 (2024) - [c20]Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt:
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. ICLR 2024 - [i44]Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola:
Improved motif-scaffolding with SE(3) flow matching. CoRR abs/2401.04082 (2024) - [i43]Julian Cremer, Tuan Le, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt:
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling. CoRR abs/2405.14925 (2024) - [i42]Maximilian Schebek, Michele Invernizzi, Frank Noé, Jutta Rogal:
Efficient mapping of phase diagrams with conditional normalizing flows. CoRR abs/2406.12378 (2024) - [i41]Leon Klein, Frank Noé:
Transferable Boltzmann Generators. CoRR abs/2406.14426 (2024) - [i40]Paolo Andrea Erdman, Robert Czupryniak, Bibek Bhandari, Andrew N. Jordan, Frank Noé, Jens Eisert, Giacomo Guarnieri:
Artificially intelligent Maxwell's demon for optimal control of open quantum systems. CoRR abs/2408.15328 (2024) - [i39]Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk Kooi, Jonas Köhler, Klaas J. H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster:
Highly Accurate Real-space Electron Densities with Neural Networks. CoRR abs/2409.01306 (2024) - [i38]Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov:
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. CoRR abs/2410.07974 (2024) - 2023
- [c19]Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé:
Rigid Body Flows for Sampling Molecular Crystal Structures. ICML 2023: 17301-17326 - [c18]Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka:
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. NeurIPS 2023 - [c17]Leon Klein, Andreas Krämer, Frank Noé:
Equivariant flow matching. NeurIPS 2023 - [i37]Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé:
Rigid body flows for sampling molecular crystal structures. CoRR abs/2301.11355 (2023) - [i36]Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zügner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg:
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. CoRR abs/2302.00600 (2023) - [i35]Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka:
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. CoRR abs/2302.01170 (2023) - [i34]Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu:
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning. CoRR abs/2306.05445 (2023) - [i33]Leon Klein, Andreas Krämer, Frank Noé:
Equivariant flow matching. CoRR abs/2306.15030 (2023) - [i32]Hao Wu, Frank Noé:
Reaction coordinate flows for model reduction of molecular kinetics. CoRR abs/2309.05878 (2023) - [i31]Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt:
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. CoRR abs/2309.17296 (2023) - 2022
- [j19]Justin Spiriti, Frank Noé, Chung F. Wong:
Simulation of ligand dissociation kinetics from the protein kinase PYK2. J. Comput. Chem. 43(28): 1911-1922 (2022) - [j18]Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer:
Generating stable molecules using imitation and reinforcement learning. Mach. Learn. Sci. Technol. 3(1): 15008 (2022) - [j17]Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé:
Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 3(1): 15009 (2022) - [c16]Tuan Le, Frank Noé, Djork-Arné Clevert:
Representation Learning on Biomolecular Structures Using Equivariant Graph Attention. LoG 2022: 30 - [c15]Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert:
Unsupervised Learning of Group Invariant and Equivariant Representations. NeurIPS 2022 - [i30]Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert:
Unsupervised Learning of Group Invariant and Equivariant Representations. CoRR abs/2202.07559 (2022) - [i29]Tuan Le, Frank Noé, Djork-Arné Clevert:
Equivariant Graph Attention Networks for Molecular Property Prediction. CoRR abs/2202.09891 (2022) - [i28]Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé:
Force-matching Coarse-Graining without Forces. CoRR abs/2203.11167 (2022) - [i27]Paolo Andrea Erdman, Frank Noé:
Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning. CoRR abs/2204.04785 (2022) - [i26]Jan Hermann, James S. Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé:
Ab-initio quantum chemistry with neural-network wavefunctions. CoRR abs/2208.12590 (2022) - [i25]Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé:
Machine learning frontier orbital energies of nanodiamonds. CoRR abs/2210.07930 (2022) - [i24]Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni De Fabritiis:
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. CoRR abs/2212.07492 (2022) - 2021
- [j16]Stefan Klus, Patrick Gelß, Feliks Nüske, Frank Noé:
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Mach. Learn. Sci. Technol. 2(4): 45016 (2021) - [j15]Margarita Kostré, Christof Schütte, Frank Noé, Mauricio J. Del Razo:
Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs. Multiscale Model. Simul. 19(4): 1659-1683 (2021) - [c14]Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert:
Parameterized Hypercomplex Graph Neural Networks for Graph Classification. ICANN (3) 2021: 204-216 - [c13]Jonas Köhler, Andreas Krämer, Frank Noé:
Smooth Normalizing Flows. NeurIPS 2021: 2796-2809 - [c12]Robin Winter, Frank Noé, Djork-Arné Clevert:
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. NeurIPS 2021: 9559-9573 - [i23]Robin Winter, Frank Noé, Djork-Arné Clevert:
Auto-Encoding Molecular Conformations. CoRR abs/2101.01618 (2021) - [i22]Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert:
Parameterized Hypercomplex Graph Neural Networks for Graph Classification. CoRR abs/2103.16584 (2021) - [i21]Robin Winter, Frank Noé, Djork-Arné Clevert:
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. CoRR abs/2104.09856 (2021) - [i20]Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer:
Generating stable molecules using imitation and reinforcement learning. CoRR abs/2107.05007 (2021) - [i19]Paolo Andrea Erdman, Frank Noé:
Identifying optimal cycles in quantum thermal machines with reinforcement-learning. CoRR abs/2108.13525 (2021) - [i18]Jonas Köhler, Andreas Krämer, Frank Noé:
Smooth Normalizing Flows. CoRR abs/2110.00351 (2021) - [i17]Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé:
Deeptime: a Python library for machine learning dynamical models from time series data. CoRR abs/2110.15013 (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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