Joel Dapello

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Scaling & Learning Theory — 8 citations into it

Whom Joel Dapello trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Kenji Fukumizu
7. Andrew M. Saxe
8. Ilya Sutskever

Papers from Joel Dapello's perspective

Ordered by how strongly the researchers Joel Dapello 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• On the information bottleneck theory of deep learning (2018) — 692 citations