Jonas Köhler
Microsoft (United States)(US)Microsoft Research (India)(IN)
Publications by Year
Research Areas
Stochastic Gradient Optimization Techniques, Advanced Numerical Methods in Computational Mathematics, Neural Networks and Applications, Sparse and Compressive Sensing Techniques, Generative Adversarial Networks and Image Synthesis
Most-Cited Works
- → Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning(2019)717 cited
- → Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning(2018)67 cited
- → Using taxonomic consistency with semi‐automated data pre‐processing for high quality DNA barcodes(2017)57 cited
- → Escaping Saddles with Stochastic Gradients(2018)57 cited
- → Convolutional Networks for Spherical Signals(2017)56 cited
- → Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces(2023)56 cited
- → Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities(2020)51 cited
- → Cross-Domain Mining of Argumentative Text through Distant Supervision(2016)50 cited
- → Equivariant Flows: sampling configurations for multi-body systems with symmetric energies(2019)45 cited
- → Sub-Sampled Cubic Regularization for Non-Convex Optimization(2017)44 cited