J. Ramon

Artificial Intelligence · 2001–2020

A researcher in the Science corpus: 11 papers; trusts 72 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #8,197 of 35,345 by trust here, #17,412 by citations
• Privacy & Security — 36 citations into it
• Molecular Biology — 25 citations into it
• Statistics and Probability — 19 citations into it
• Statistical Learning Theory — 15 citations into it; #487 of 1,038 by trust here, #662 by citations
• Computer Networks and Communications — 14 citations into it; #6,420 of 9,886 by trust here, #8,844 by citations
• Computer Vision and Pattern Recognition — 14 citations into it; #12,278 of 21,575 by trust here, #19,310 by citations
• Reinforcement Learning — 12 citations into it

Whom J. Ramon trusts overall

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

1. Bernhard Schölkopf
2. Leslie Pack Kaelbling
3. Kurt Driessens
4. Thomas Gärtner
5. Ben J. A. Kröse
6. Thomas Eiter
7. Thomas G. Dietterich
8. W. Hoeffding
9. Anthony Dewayne Hunt
10. Cynthia Dwork

Who trusts J. Ramon overall

1. Kurt Driessens
2. Kristian Kersting
3. Martijn van Otterlo
4. Nils M. Kriege
5. Prasad Tadepalli
6. Christopher Morris
7. Pinar Yanardag
8. S. V. N. Vishwanathan
9. Robert Givan
10. Fredrik D. Johansson

Papers from J. Ramon's perspective

Ordered by how strongly the researchers J. Ramon 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
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

• Graph kernels and Gaussian processes for relational reinforcement learning (2006) — 109 citations
• Learning relational dependency networks in hybrid domains (2015) — 25 citations
• Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties (2020) — 26 citations
• Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries (2018) — 8 citations
• Hoeffding's inequality for sums of weakly dependent random variables (2015) — 9 citations
• A new ensemble coevolution system for detecting HIV-1 protein coevolution (2015) — 601 citations
• Machine learning techniques to examine large patient databases. (2009) — 120 citations
• Relational Instance Based Regression for Relational Reinforcement Learning (2003) — 85 citations
• Expressivity versus efficiency of graph kernels (2003) — 253 citations
• Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner (2001) — 96 citations
• A polynomial time computable metric between point sets (2001) — 104 citations