Peter Wittek

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 2 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it
• Atomic and Molecular Physics, and Optics — 31 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Statistical Learning Theory — 12 citations into it
• Model Architectures — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Peter Wittek trusts overall

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

1. Vladimir Naumovich Vapni
2. Jerome H. Friedman
3. Vladimir Naumovich Vapnik
4. Thorsten Joachims
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Seth Lloyd
8. Leo Breiman
9. Robert E. Schapire
10. Yann LeCun

Who trusts Peter Wittek overall

1. Ryan Larose
2. Hanrui Wang
3. M. Krelina
4. Brian Coyle
5. Vedran Dunjko
6. Maria Schuld
7. Roman Orus
8. Yongshan Ding
9. Seth Lloyd
10. S. Nawaz

Papers from Peter Wittek's perspective

Ordered by how strongly the researchers Peter Wittek 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Quantum machine learning (2016) — 2,780 citations
• Quantum Machine Learning: What Quantum Computing Means to Data Mining (2014) — 425 citations