Antti Honkela

Artificial Intelligence · 2000–2014

A researcher in the Science corpus: 6 papers; trusts 13 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #9,698 of 35,345 by trust here, #13,962 by citations
• Probabilistic & Bayesian Methods — 12 citations into it; #344 of 1,525 by trust here, #543 by citations
• Statistics and Probability — 10 citations into it
• Computational Theory and Mathematics — 8 citations into it; #9,428 of 12,182 by trust here, #3,218 by citations
• Optimization & Training — 8 citations into it
• Signal Processing — 5 citations into it; #2,302 of 7,572 by trust here, #4,201 by citations

Whom Antti Honkela trusts overall

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

1. Masa-aki Sato
2. Shun-ichi Amari
3. Geoffrey E. Hinton
4. Zoubin Ghahramani
5. Harri Valpola
6. Radford M. Neal
7. Michael I. Jordan
8. Juha Karhunen
9. Alan Edelman
10. Charles M. Bishop

Who trusts Antti Honkela overall

1. Thang D. Bui
2. Yarin Gal
3. Cuong V Nguyen
4. David A. Knowles
5. David Rohde
6. Michalis K. Titsias
7. David J. Nott
8. Cheng Zhang
9. Razvan Pascanu
10. Miguel L'azaro-Gredilla

Papers from Antti Honkela's perspective

Ordered by how strongly the researchers Antti Honkela 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes (2010) — 114 citations
• Natural Conjugate Gradient in Variational Inference (2007) — 51 citations
• Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities (2008) — 127 citations
• A community effort to assess and improve drug sensitivity prediction algorithms (2014) — 728 citations
• On-line Variational Bayesian Learning (2003) — 91 citations
• Nonlinear Independent Component Analysis Using Ensemble Learning: Experiments and Discussion (2000) — 42 citations