Jonathan Ko

Artificial Intelligence · 2005–2009

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #3,269 of 35,345 by trust here, #9,092 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Control and Systems Engineering — 12 citations into it; #2,108 of 8,257 by trust here, #4,722 by citations
• Reinforcement Learning — 9 citations into it
• Aerospace Engineering — 8 citations into it; #1,283 of 6,421 by trust here, #2,449 by citations
• Probabilistic & Bayesian Methods — 8 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Jonathan Ko trusts overall

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

1. Neil D. Lawrence
2. Sebastian Thrun
3. Dieter Fox
4. Brian Ferris
5. Andrew Y. Ng
6. Edward Snelson
7. Kristian Kersting
8. Geoffrey E. Hinton
9. Wolfram Burgard
10. Bernhard Schölkopf

Who trusts Jonathan Ko overall

1. Marc Peter Deisenroth
2. Carl E. Rasmussen
3. Dieter Fox
4. Rowan Thomas McAllister
5. Sergey Levine
6. Anusha Nagabandi
7. Jan Peters
8. Pete Trautman
9. Axel Rottmann
10. Pieter Abbeel

Papers from Jonathan Ko's perspective

Ordered by how strongly the researchers Jonathan Ko 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models (2008) — 449 citations
• Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp (2007) — 225 citations
• Learning GP-BayesFilters via Gaussian process latent variable models (2009) — 108 citations
• Distributed Multirobot Exploration and Mapping (2005) — 411 citations