Pasi Fränti
Artificial Intelligence · 2004–2019
A researcher in the Science corpus: 7 papers; trusts 30 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 28 citations into it; #11,934 of 35,345 by trust here, #14,181 by citations
- Signal Processing — 16 citations into it; #2,746 of 7,572 by trust here, #3,566 by citations
- Computer Vision and Pattern Recognition — 14 citations into it; #10,923 of 21,575 by trust here, #12,419 by citations
- Statistics and Probability — 8 citations into it
- Algorithms & Complexity — 7 citations into it
Whom Pasi Fränti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Anil K. Jain
- Ulrike von Luxburg
- Teuvo Kohonen
- Teofilo F. Gonzalez
- Michael S. Steinbach
- Daniel Boley
- Robert M. Gray
- David Arthur
- Aristidis C. Likas
- Tian Zhang
Who trusts Pasi Fränti overall
- Juanying Xie
- Michael Hahsler
- Sami Sieranoja
- Hongchao Gao
- Zhen Wang
- Mikko I. Malinen
- Matt Piekenbrock
- W. Xie
- Xiaohui Liu
- Rui Zhang
Papers from Pasi Fränti's perspective
Ordered by how strongly the researchers Pasi Fränti 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Algorithms for Clustering Data
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- Improving K-Means by Outlier Removal (2005) — 144 citations
- How much can k-means be improved by using better initialization and repeats? (2019) — 418 citations
- K-means properties on six clustering benchmark datasets (2018) — 472 citations
- Balanced K-Means for Clustering (2014) — 205 citations
- Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph (2006) — 307 citations
- Iterative shrinking method for clustering problems (2006) — 350 citations
- Outlier Detection Using k-Nearest Neighbour Graph (2004) — 402 citations