This map shows the geographic impact of Antti Honkela'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 Antti Honkela with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Antti Honkela more than expected).
This network shows the impact of papers produced by Antti Honkela. 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 Antti Honkela. The network helps show where Antti Honkela may publish in the future.
Co-authorship network of co-authors of Antti Honkela
This figure shows the co-authorship network connecting the top 25 collaborators of Antti Honkela.
A scholar is included among the top collaborators of Antti Honkela 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 Antti Honkela. Antti Honkela is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kulkarni, Tejas, et al.. (2021). Differentially Private Bayesian Inference for Generalized Linear Models. Research Explorer (The University of Manchester). 5838–5849.4 indexed citations
5.
Koskela, Antti, et al.. (2020). Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT.. arXiv (Cornell University).5 indexed citations
6.
Koskela, Antti & Antti Honkela. (2020). Learning Rate Adaptation for Differentially Private Learning. Työväentutkimus Vuosikirja. 2465–2475.3 indexed citations
7.
Koskela, Antti, et al.. (2019). Computing Tight Differential Privacy Guarantees Using FFT. Työväentutkimus Vuosikirja. 2560–2569.1 indexed citations
8.
Koskela, Antti & Antti Honkela. (2018). Learning rate adaptation for differentially private stochastic gradient descent. arXiv (Cornell University).4 indexed citations
Honkela, Antti, et al.. (2010). Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes. Journal of Machine Learning Research. 11(106). 3235–3268.48 indexed citations
14.
Honkela, Antti, et al.. (2007). Agglomerative Independent Variable Group Analysis.. The European Symposium on Artificial Neural Networks. 55–60.1 indexed citations
15.
Honkela, Antti, et al.. (2005). Empirical evidence of the linear nature of magnetoencephalograms. The European Symposium on Artificial Neural Networks. 285–290.4 indexed citations
16.
Lagus, Krista, et al.. (2005). Independent Variable Group Analysis in Learning Compact Representations for Data.6 indexed citations
17.
Honkela, Antti & Harri Valpola. (2004). Unsupervised Variational Bayesian Learning of Nonlinear Models. Neural Information Processing Systems. 17. 593–600.31 indexed citations
Valpola, Harri, et al.. (2000). Nonlinear Independent Component Analysis Using Ensemble Learning: Experiments and Discussion.19 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.