Marcus Frean

Artificial Intelligence · 1998–2017

A researcher in the Science corpus: 7 papers; trusts 16 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #11,347 of 35,345 by trust here, #7,728 by citations
• Optimization & Training — 21 citations into it
• Model Architectures — 17 citations into it; #1,929 of 2,278 by trust here, #1,168 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #18,666 of 21,575 by trust here, #8,409 by citations
• Statistical Learning Theory — 10 citations into it
• Statistics and Probability — 6 citations into it

Whom Marcus Frean trusts overall

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

1. J. van Hemmen
2. Kaiming He
3. Jerome H. Friedman
4. Leo Breiman
5. Anthony V. Robins
6. Robert E. Schapire
7. Yoav Freund
8. Yoshua Bengio
9. Geoffrey E. Hinton
10. D. Higdon

Who trusts Marcus Frean overall

1. Mauricio A Álvarez
2. John A. Bullinaria
3. Anthony V. Robins
4. Neil D. Lawrence
5. Phillip Boyle
6. Michalis K. Titsias
7. Nathan D. Ratliff
8. Yoshua Bengio
9. Lorenzo Rosasco
10. Yann Dauphin

Papers from Marcus Frean's perspective

Ordered by how strongly the researchers Marcus Frean 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. Going deeper with convolutions
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The Shattered Gradients Problem: If resnets are the answer, then what is the question? (2017) — 465 citations
• Functional Gradient Techniques for Combining Hypotheses (2000) — 341 citations
• Boosting Algorithms as Gradient Descent in Function Space (2007) — 242 citations
• Dependent Gaussian Processes (2004) — 316 citations
• Boosting Algorithms as Gradient Descent (1999)
• Catastrophic forgetting in simple networks: an analysis of the pseudorehearsal solution. (1999)
• General cost functions for support vector regression. (1998)