Samuel Ritter

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 87 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #21,881 of 35,345 by trust here, #11,624 by citations
• Reinforcement Learning — 74 citations into it
• Cognitive Neuroscience — 56 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it; #13,594 of 21,575 by trust here, #11,950 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Natural Language Processing — 20 citations into it
• Developmental and Educational Psychology — 13 citations into it
• Model Architectures — 13 citations into it

Whom Samuel Ritter trusts overall

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

1. Geoffrey E. Hinton
2. Sepp Hochreiter
3. Yoshua Bengio
4. Alex Graves
5. Richard S. Sutton
6. J. Urgen Schmidhuber
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Yann LeCun
10. Oriol Vinyals

Who trusts Samuel Ritter overall

1. Felix Hill
2. Andrew Kyle Lampinen
3. Hossein Hosseini
4. Marcel Binz
5. Ishita Dasgupta
6. John P. Lalor
7. Magdalena Biesialska
8. Nathan Hilliard
9. Pierre Stock
10. Luckeciano Carvalho Melo

Papers from Samuel Ritter's perspective

Ordered by how strongly the researchers Samuel Ritter 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study (2017) — 207 citations
• Been There, Done That: Meta-Learning with Episodic Recall (2018) — 95 citations
• Synthetic Returns for Long-Term Credit Assignment (2021) — 36 citations
• Rapid Task-Solving in Novel Environments (2020) — 31 citations
• Reinforcement Learning, Fast and Slow. (2019) — 659 citations
• Toward a universal decoder of linguistic meaning from brain activation (2018) — 379 citations