Joel Schlosser

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 1 paper; trusts 49 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 23 citations into it
• Artificial Intelligence — 20 citations into it; #20,513 of 35,345 by trust here, #23,731 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Optimization & Training — 9 citations into it

Whom Joel Schlosser trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Kaiming He
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Karen Simonyan
7. Yoshua Bengio
8. X. Zhang
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Joel Schlosser overall

1. Kai Han
2. K. J. Joseph
3. Zhun Zhong

Papers from Joel Schlosser's perspective

Ordered by how strongly the researchers Joel Schlosser 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Going deeper with convolutions

Their papers

• Multi-class Classification without Multi-class Labels (2019) — 200 citations