Antti Airola
Artificial Intelligence · 2008–2014
A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 22 citations into it
- Computer Vision and Pattern Recognition — 10 citations into it
- Statistical Learning Theory — 6 citations into it
- Representation & Self-Supervised Learning — 5 citations into it
Whom Antti Airola trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Razvan C. Bunescu
- Christopher K. I. Williams
- Olivier Chapelle
- Thorsten Joachims
- Raymond J. Mooney
- Johan A. K. Suykens
- Chih-Jen Lin
- Dmitry Zelenko
- Thomas Gärtner
- Bernhard Schölkopf
Who trusts Antti Airola overall
- Zhiyong Lu
- Yuan Luo
- Lei Hua
- Ozlem Uzuner
- Yifan Peng
- Isabel Segura-Bedmar
- Domonkos Tikk
- Farrokh Mehryary
- Peter Szolovits
- Chanqin Quan
Papers from Antti Airola's perspective
Ordered by how strongly the researchers Antti Airola trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Fast and simple gradient-based optimization for semi-supervised support vector machines (2014) — 60 citations
- All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning (2008) — 313 citations
- Toward more realistic drug–target interaction predictions (2014) — 514 citations