Edward Meeds

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 7 papers; trusts 124 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #3,260 of 35,345 by trust here, #8,007 by citations
• Probabilistic & Bayesian Methods — 48 citations into it; #426 of 1,525 by trust here, #991 by citations
• Computer Vision and Pattern Recognition — 33 citations into it; #4,978 of 21,575 by trust here, #7,499 by citations
• Statistics and Probability — 33 citations into it; #1,517 of 12,932 by trust here, #4,132 by citations
• Optimization & Training — 30 citations into it
• Efficiency & Compression — 26 citations into it; #222 of 1,264 by trust here, #588 by citations
• Model Architectures — 14 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Edward Meeds trusts overall

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

1. Scott Anthony Sisson
2. Max Welling
3. Simon N. Wood
4. MARK A. Beaumont
5. Radford M. Neal
6. Yanan Fan
7. Alex Graves
8. Geoffrey E. Hinton
9. Kaiming He
10. Yoshua Bengio

Who trusts Edward Meeds overall

1. George Papamakarios
2. Iain Murray
3. Joeri Hermans
4. Gilles Louppe
5. Jan-Matthis Lueckmann
6. Matthew M. Graham
7. Michael U Gutmann
8. David C. Sterratt
9. Volodimir Begy
10. Amos J. Storkey

Papers from Edward Meeds's perspective

Ordered by how strongly the researchers Edward Meeds 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Soft Weight-Sharing for Neural Network Compression (2017) — 444 citations
• Hamiltonian ABC (2015) — 32 citations
• Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference (2015) — 36 citations
• GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation (2014) — 127 citations
• Deterministic Variational Inference for Robust Bayesian Neural Networks (2018) — 185 citations
• Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks (2018) — 16 citations
• Distributed under Creative Commons Cc-by 4.0 Mlitb: Machine Learning in the Browser