Ard A. Louis

Artificial Intelligence · 2000–2021

A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #26,276 of 35,345 by trust here, #21,878 by citations
• Scaling & Learning Theory — 28 citations into it; #323 of 428 by trust here, #336 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #16,111 of 21,575 by trust here, #14,478 by citations
• Optimization & Training — 14 citations into it
• Statistical Learning Theory — 8 citations into it
• Materials Chemistry — 8 citations into it; #9,138 of 11,513 by trust here, #10,845 by citations
• Statistical and Nonlinear Physics — 8 citations into it
• Model Architectures — 6 citations into it

Whom Ard A. Louis trusts overall

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

1. J. Urgen Schmidhuber
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. L. Eon Bottou
7. Jimmy Ba
8. Ilya Sutskever
9. Diederik P. Kingma
10. Vladimir Naumovich Vapni

Who trusts Ard A. Louis overall

1. Marcus Mueller
2. K. Katsov
3. Christos N. Likos
4. Michael Schick
5. ST John
6. W. Noid
7. Greg Yang
8. SY Sergey Trofimov
9. Marcus Müller

Papers from Ard A. Louis's perspective

Ordered by how strongly the researchers Ard A. Louis 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep learning generalizes because the parameter-function map is biased towards simple functions (2018) — 293 citations
• Accurate effective pair potentials for polymer solutions (2000) — 271 citations
• Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution (2021) — 105 citations
• Beware of density dependent pair potentials (2002) — 311 citations