Seth Baer

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 57 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Generative Models — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Seth Baer trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Alex Krizhevsky
6. Ilya Sutskever
7. Radford M. Neal
8. Kaiming He
9. Jimmy Ba
10. Jost Tobias Springenberg

Who trusts Seth Baer overall

1. Zachary Alan Daniels
2. James Smith
3. Yanbei Chen

Papers from Seth Baer's perspective

Ordered by how strongly the researchers Seth Baer 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Unsupervised Progressive Learning and the STAM Architecture (2019) — 41 citations
• Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies (2019) — 12 citations