Okko Johannes Räsänen
Developmental and Educational Psychology · 2012–2015
A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Developmental and Educational Psychology — 15 citations into it; #1,067 of 8,223 by trust here, #4,146 by citations
- Artificial Intelligence — 13 citations into it; #7,450 of 35,345 by trust here, #21,878 by citations
- Experimental and Cognitive Psychology — 11 citations into it; #1,279 of 9,731 by trust here, #5,375 by citations
- Speech & Audio — 9 citations into it; #631 of 1,076 by trust here, #960 by citations
- Signal Processing — 8 citations into it
- Cultural Studies — 7 citations into it
Whom Okko Johannes Räsänen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Teuvo Kohonen
- Mark Newman
- Patricia K. Kuhl
- Alex Seungryong Park
- Arthur P. Dempster
- Naomi H Feldman
- Oded Ghitza
- James R. Glass
- Janet F. Werker
- Luc Steels
Who trusts Okko Johannes Räsänen overall
- Herman Kamper
- Emmanuel Dupoux
- Aren Jansen
- Sharon Goldwater
- Benjamin van Niekerk
- Yu-An Chung
- Alexei Baevski
- Leanne Nortje
- Maarten Versteegh
- Karen Livescu
Papers from Okko Johannes Räsänen's perspective
Ordered by how strongly the researchers Okko Johannes Räsänen trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Cross-language speech perception: Evidence for perceptual reorganization during the first year of life
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)