Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
2019394 citationsFrank Noé, Simon Olsson et al.Scienceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jonas Köhler's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jonas Köhler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Köhler more than expected).
This network shows the impact of papers produced by Jonas Köhler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jonas Köhler. The network helps show where Jonas Köhler may publish in the future.
Co-authorship network of co-authors of Jonas Köhler
This figure shows the co-authorship network connecting the top 25 collaborators of Jonas Köhler.
A scholar is included among the top collaborators of Jonas Köhler based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jonas Köhler. Jonas Köhler is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Daneshmand, Hadi, Jonas Köhler, Francis Bach, Thomas Hofmann, & Aurélien Lucchi. (2020). Batch normalization provably avoids ranks collapse for randomly initialised deep networks. Neural Information Processing Systems. 33. 18387–18398.8 indexed citations
12.
Wu, Hao, Jonas Köhler, & Frank Noé. (2020). Stochastic Normalizing Flows. Neural Information Processing Systems. 33. 5933–5944.4 indexed citations
13.
Daneshmand, Hadi, Jonas Köhler, Francis Bach, Thomas Hofmann, & Aurélien Lucchi. (2020). Theoretical Understanding of Batch-normalization: A Markov Chain Perspective..3 indexed citations
Noé, Frank, Simon Olsson, Jonas Köhler, & Hao Wu. (2019). Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. Science. 365(6457).394 indexed citations breakdown →
16.
Köhler, Jonas, et al.. (2019). Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training.. arXiv (Cornell University).2 indexed citations
Daneshmand, Hadi, Jonas Köhler, Aurélien Lucchi, & Thomas Hofmann. (2018). Escaping Saddles with Stochastic Gradients. International Conference on Machine Learning. 1155–1164.6 indexed citations
19.
Köhler, Jonas, et al.. (2018). Towards a Theoretical Understanding of Batch Normalization..14 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.