Samuel Kaski

Artificial Intelligence · 2000–2018

A researcher in the Science corpus: 13 papers; trusts 103 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #7,665 of 35,345 by trust here, #7,797 by citations
• Statistics and Probability — 60 citations into it; #3,906 of 12,932 by trust here, #3,038 by citations
• Probabilistic & Bayesian Methods — 56 citations into it; #574 of 1,525 by trust here, #558 by citations
• Computer Vision and Pattern Recognition — 44 citations into it; #7,877 of 21,575 by trust here, #10,905 by citations
• Representation & Self-Supervised Learning — 33 citations into it; #1,670 of 3,145 by trust here, #2,148 by citations
• Molecular Biology — 26 citations into it
• Statistical Learning Theory — 14 citations into it
• Optimization & Training — 12 citations into it

Whom Samuel Kaski trusts overall

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

1. Geoffrey E. Hinton
2. Teuvo Kohonen
3. Michael U Gutmann
4. Alex Krizhevsky
5. Radford M. Neal
6. Tom L. Burr
7. Eric R. Ziegel
8. Francis R. Bach
9. Christopher Meek
10. Michael I. Jordan

Who trusts Samuel Kaski overall

1. Greg Yang
2. Aapo Hyvärinen
3. Guilherme de Alencar Barreto
4. Johan Himberg
5. Leland McInnes
6. Teuvo Kohonen
7. Gilles Louppe
8. John Aldo Lee
9. I. Fodor
10. Joeri Hermans

Papers from Samuel Kaski's perspective

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

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

Their papers

• Likelihood-free inference via classification (2014) — 136 citations
• Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization (2010) — 407 citations
• Deep convolutional Gaussian processes (2018) — 65 citations
• Likelihood-Free Inference by Ratio Estimation (2016) — 180 citations
• Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study (2001) — 324 citations
• Bayesian Canonical correlation analysis (2013) — 192 citations
• A community effort to assess and improve drug sensitivity prediction algorithms (2014) — 728 citations
• Bayesian Networks for Variable Groups (2015) — 10 citations
• Learning structures of Bayesian networks for variable groups (2015) — 41 citations
• Bayesian exponential family projections for coupled data sources (2010) — 33 citations
• Local Multidimensional Scaling with Controlled Tradeoff Between Trustworthiness and Continuity (2005) — 50 citations
• Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (2003) — 406 citations
• Self organization of a massive document collection (2000) — 1,057 citations