Kari Torkkola

Artificial Intelligence · 2003–2019

A researcher in the Science corpus: 4 papers; trusts 37 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #6,816 of 35,345 by trust here, #10,798 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #14,551 of 21,575 by trust here, #20,756 by citations
• Generative Models — 12 citations into it
• Statistics and Probability — 12 citations into it
• Signal Processing — 11 citations into it; #1,348 of 7,572 by trust here, #2,276 by citations
• Management Science and Operations Research — 9 citations into it; #2,699 of 11,946 by trust here, #3,153 by citations
• Model Architectures — 9 citations into it
• Statistical Learning Theory — 8 citations into it

Whom Kari Torkkola trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Emanuel Parzen
2. Sanjoy Dasgupta
3. Flemming Topsøe
4. Ronan Collobert
5. Bernhard Schölkopf
6. Deniz Erdoğmuş
7. Jason Aaron Edward Weston
8. Samy Bengio
9. Yoshua Bengio
10. Geoffrey E. Hinton

Who trusts Kari Torkkola overall

1. David Wingate
2. Nikolaos Passalis
3. Franccois Fleuret
4. José Carlos Príncipe
5. Dacheng Tao
6. Thomas Villmann
7. Taiji Suzuki
8. Satinder Singh
9. Girish Chandrashekar
10. Tarek Amr Abdallah

Papers from Kari Torkkola's perspective

Ordered by how strongly the researchers Kari Torkkola trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• A Multi-Horizon Quantile Recurrent Forecaster (2017) — 527 citations
• Deep Generative Quantile-Copula Models for Probabilistic Forecasting (2019) — 38 citations
• Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination (2009) — 346 citations
• Feature Extraction by Non-Parametric Mutual Information Maximization (2003) — 724 citations