Harri Valpola
- Signal Processing top 2%
- Blind Source Separation Techniques 21
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- Advanced Neural Network Applications 3
- Image and Signal Denoising Methods 2
- Artificial Intelligence top 1%
- Neural Networks and Applications 15
- Analytical Chemistry top 2%
- Spectroscopy and Chemometric Analyses 10
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function 2
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- Fault Detection and Control Systems 6
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- Climate variability and models 3
- Co-authors
- Juha KarhunenTapani RaikoYann LeCunAntti HonkelaJaakko SäreläErkki OjaAlexander IlinTommi Vatanen
- Partner nations
- FinlandRussiaUnited States
In The Last Decade
Harri Valpola
38 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 120
- Signal Processing 447
- Computer Vision and Pattern Recognition 667
- Artificial Intelligence 929
- Analytical Chemistry 182
- Cognitive Neuroscience 140
Countries citing papers authored by Harri Valpola
This map shows the geographic impact of Harri Valpola'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 Harri Valpola with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harri Valpola more than expected).
Fields of papers citing papers by Harri Valpola
This network shows the impact of papers produced by Harri Valpola. 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 Harri Valpola. The network helps show where Harri Valpola may publish in the future.
Co-authorship network
The 17 scholars most cited alongside Harri Valpola, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Weight-averaged consistency targets improve semi-supervised deep learning results. | 2017 | 38 |
| 2 | Denoising autoencoder with modulated lateral connections learns invariant representations of natural images | 2014 | 1 |
| 3 | Deep Learning Made Easier by Linear Transformations in Perceptrons | 2012 | 87 |
| 4 | 2011 | 6 | |
| 5 | Building Blocks for Variational Bayesian Learning of Latent Variable Models | 2007 | 15 |
| 6 | 2006 | 17 | |
| 7 | 2006 | 6 | |
| 8 | Denoising Source Separation | 2005 | 111 |
| 9 | 2005 | 15 | |
| 10 | Behaviourally Meaningful Representations From Normalisation And Context-Guided Denoising | 2004 | 3 |
| 11 | Unsupervised Variational Bayesian Learning of Nonlinear Models | 2004 | 31 |
| 12 | 2004 | 12 | |
| 13 | 2004 | 33 | |
| 14 | Bayes Blocks Software Library | 2003 | 5 |
| 15 | 2003 | 16 | |
| 16 | Missing Values in Hierarchical Nonlinear Factor Analysis | 2003 | 6 |
| 17 | 2003 | 23 | |
| 18 | DETECTING PROCESS STATE CHANGES BY NONLINEAR BLIND SOURCE SEPARATION | 2003 | 9 |
| 19 | Artefact Detection In Astrophysical Image Data Using Independent Component Analysis | 2001 | 1 |
| 20 | Dynamical Factor Analysis Of Rhythmic Magnetoencephalographic Activity | 2001 | 8 |
About Harri Valpola
Harri Valpola is a scholar working on Signal Processing, Analytical Chemistry and Artificial Intelligence, having authored 41 papers that have together received 1.6k indexed citations. Recurring topics across this work include Blind Source Separation Techniques (21 papers), Neural Networks and Applications (15 papers), Spectroscopy and Chemometric Analyses (10 papers), Fault Detection and Control Systems (6 papers), Advanced Neural Network Applications (3 papers), Climate variability and models (3 papers), Image and Signal Denoising Methods (2 papers) and Neural dynamics and brain function (2 papers). The work is most often cited by research in Signal Processing (447 citations), Computer Vision and Pattern Recognition (667 citations) and Artificial Intelligence (929 citations). Harri Valpola has collaborated with scholars based in Finland, Russia and United States. Frequent co-authors include Juha Karhunen, Tapani Raiko, Yann LeCun, Antti Honkela, Jaakko Särelä, Erkki Oja, Alexander Ilin, Tommi Vatanen, А. А. Ильин and Timo Honkela.
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.