Jon Barker

Artificial Intelligence · with Brandon · 2017–2024

A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 10 citations into it

Whom Jon Barker trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ross B. Girshick
6. Yann LeCun
7. Christian Szegedy
8. Yoshua Bengio
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Jon Barker overall

1. H. S. Anderson

Papers from Jon Barker's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• NVLM: Open Frontier-Class Multimodal LLMs (2024)
• Malware Detection by Eating a Whole EXE (2017) — 690 citations