Neal Jean

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 4 papers; trusts 63 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 29 citations into it
• Artificial Intelligence — 21 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Neal Jean trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Yoshua Bengio
4. Alex Krizhevsky
5. Radford M. Neal
6. Yann LeCun
7. Kaiming He
8. Karen Simonyan
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Neal Jean overall

1. Maruan Al-Shedivat
2. Jiaxuan You
3. Timnit Gebru
4. Yair Rivenson
5. Mei Wang
6. Ananya Kumar
7. Kumar Avinava Dubey
8. Nenad Tomašev
9. Wen Zhang
10. Shuteng Niu

Papers from Neal Jean's perspective

Ordered by how strongly the researchers Neal Jean 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping (2015) — 458 citations
• Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning (2019) — 715 citations
• Combining satellite imagery and machine learning to predict poverty (2016) — 1,609 citations
• Semi-supervised deep kernel learning (2016)