Richard Meyes

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 65 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Safety, Robustness & Interpretability — 18 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Richard Meyes trusts overall

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

1. Geoffrey E. Hinton
2. L. V. D. van der Maaten
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Kaiming He
7. Ilya Sutskever
8. Diederik P. Kingma
9. Alex Graves
10. Karen Simonyan

Papers from Richard Meyes's perspective

Ordered by how strongly the researchers Richard Meyes 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations (2020) — 22 citations
• Ablation Studies in Artificial Neural Networks (2019) — 303 citations