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.
This map shows the geographic impact of Tapani Raiko'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 Tapani Raiko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tapani Raiko more than expected).
This network shows the impact of papers produced by Tapani Raiko. 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 Tapani Raiko. The network helps show where Tapani Raiko may publish in the future.
Co-authorship network of co-authors of Tapani Raiko
This figure shows the co-authorship network connecting the top 25 collaborators of Tapani Raiko.
A scholar is included among the top collaborators of Tapani Raiko 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 Tapani Raiko. Tapani Raiko is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Kivinen, Jyri, et al.. (2016). Understanding Regularization by Virtual Adversarial Training, Ladder Networks and Others. International Conference on Learning Representations.2 indexed citations
2.
Luketina, Jelena, Mathias Berglund, Klaus Greff, & Tapani Raiko. (2016). 33rd International Conference on Machine Learning, ICML 2016. International Conference on Machine Learning.113 indexed citations
3.
Sønderby, Casper Kaae, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, & Ole Winther. (2016). How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks. arXiv (Cornell University).33 indexed citations
4.
Luketina, Jelena, Mathias Berglund, Klaus Greff, & Tapani Raiko. (2016). Scalable gradient-based tuning of continuous regularization hyperparameters. International Conference on Machine Learning. 2952–2960.22 indexed citations
5.
Berglund, Mathias, et al.. (2015). Bidirectional recurrent neural networks as generative models. Neural Information Processing Systems. 28. 856–864.65 indexed citations
Rasmus, Antti, Tapani Raiko, & Harri Valpola. (2014). Denoising autoencoder with modulated lateral connections learns invariant representations of natural images. International Conference on Learning Representations.1 indexed citations
8.
Raiko, Tapani, et al.. (2014). European conference on machine learning and knowledge discovery in databases.65 indexed citations
Raiko, Tapani, Harri Valpola, & Yann LeCun. (2012). Deep Learning Made Easier by Linear Transformations in Perceptrons. International Conference on Artificial Intelligence and Statistics. 924–932.87 indexed citations
11.
Cho, Kyunghyun, Tapani Raiko, Alexander Ilin, & Juha Karhunen. (2012). NIPS 2012 Workshop on Deep Learning and Unsupervised Feature Learning, Lake Tahoe, Usa, December 8, 2012.1 indexed citations
12.
Cho, Kyunghyun, Tapani Raiko, & Alexander Ihler. (2011). Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines. International Conference on Machine Learning. 105–112.48 indexed citations
13.
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.
Ilin, Alexander & Tapani Raiko. (2010). Practical Approaches to Principal Component Analysis in the Presence of Missing Values. Journal of Machine Learning Research. 11(66). 1957–2000.261 indexed citations
Ilin, Alexander, et al.. (2009). Transformations for Variational Factor Analysis to Speed up Learning. The European Symposium on Artificial Neural Networks.3 indexed citations
17.
Raiko, Tapani, et al.. (2007). Building Blocks for Variational Bayesian Learning of Latent Variable Models. Journal of Machine Learning Research. 8(6). 155–201.15 indexed citations
Raiko, Tapani, et al.. (2003). Missing Values in Hierarchical Nonlinear Factor Analysis.6 indexed citations
20.
Kersting, Kristian, Tapani Raiko, & Luc De Raedt. (2002). Logical Hidden Markov Models (Extended Abstract). 99–107.1 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.