Peyman Adibi

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Statistical Learning Theory — 12 citations into it
• Model Architectures — 9 citations into it
• Statistics and Probability — 9 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Peyman Adibi trusts overall

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

1. Radford M. Neal
2. Leo Breiman
3. Christopher K. I. Williams
4. J. Urgen Schmidhuber
5. Kunihiko Fukushima
6. Ronald J. Williams
7. Yann LeCun
8. Andrew McCallum
9. David Barber

Papers from Peyman Adibi's perspective

Ordered by how strongly the researchers Peyman Adibi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern 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. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
9. Deep Learning
10. Pattern Recognition and Machine Learning

Their papers

• Machine learning for Internet of Things data analysis: A survey (2017) — 970 citations