Jacob Jackson

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #17,253 of 35,345 by trust here, #8,439 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #10,927 of 21,575 by trust here, #6,031 by citations
• Representation & Self-Supervised Learning — 14 citations into it; #1,821 of 3,145 by trust here, #3,009 by citations
• Optimization & Training — 12 citations into it
• Model Architectures — 10 citations into it

Whom Jacob Jackson trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Sergey Ioffe
4. Sugato Basu
5. Xavier Gastaldi
6. Gabriel Goh
7. Alex Krizhevsky
8. Vinod Nair
9. Yoshua Bengio
10. Christian Szegedy

Who trusts Jacob Jackson overall

1. Qizhe Xie
2. Zihang Dai
3. Eduard H. Hovy
4. Minh-Thang Luong
5. Quoc V. Le

Papers from Jacob Jackson's perspective

Ordered by how strongly the researchers Jacob Jackson 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Scaling Laws for Autoregressive Generative Modeling (2020) — 639 citations
• Semi-Supervised Learning by Label Gradient Alignment (2019) — 20 citations