Mark D. McDonnell

Computer Vision and Pattern Recognition · 2009–2016

A researcher in the Science corpus: 4 papers; trusts 152 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 28 citations into it; #19,245 of 21,575 by trust here, #9,305 by citations
• Cognitive Neuroscience — 22 citations into it; #9,722 of 10,852 by trust here, #6,526 by citations
• Artificial Intelligence — 20 citations into it; #22,638 of 35,345 by trust here, #12,580 by citations
• Statistical and Nonlinear Physics — 10 citations into it; #9,412 of 10,127 by trust here, #4,820 by citations
• Model Architectures — 7 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Mark D. McDonnell trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Pierre Sermanet
4. Geoffrey E. Hinton
5. Adam Coates
6. Chris Eliasmith
7. G. Huang
8. Andrew Y. Ng
9. Barbara Olshausen
10. Yoshua Bengio

Who trusts Mark D. McDonnell overall

1. Sebastien C. Wong
2. Yu Cheng
3. Wolfgang Maass
4. Nienke E. R. van Bueren
5. Adam Gatt
6. Felix X. Yu
7. Elahe Arani
8. Tino Zaehle
9. Victor Stamatescu

Papers from Mark D. McDonnell's perspective

Ordered by how strongly the researchers Mark D. McDonnell 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Understanding Data Augmentation for Classification: When to Warp? (2016) — 974 citations
• What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009) — 780 citations
• Enhanced image classification with a fast-learning shallow convolutional neural network (2015) — 85 citations
• The benefits of noise in neural systems: bridging theory and experiment (2011) — 742 citations