James Smith

Artificial Intelligence · with Kira · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 451 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 253 citations into it; #26,318 of 35,345 by trust here, #10,207 by citations
• Computer Vision and Pattern Recognition — 192 citations into it
• Meta & Continual Learning — 136 citations into it; #819 of 1,041 by trust here, #333 by citations
• Representation & Self-Supervised Learning — 74 citations into it
• Reinforcement Learning — 35 citations into it
• Optimization & Training — 33 citations into it
• Generative Models — 23 citations into it
• Cognitive Neuroscience — 20 citations into it

Whom James Smith trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Marc'Aurelio Ranzato
9. Ross B. Girshick
10. Jimmy Ba

Who trusts James Smith overall

1. Da-Wei Zhou
2. Zachary Alan Daniels
3. Liyuan Wang
4. Yen-Chang Hsu
5. Yan-Shuo Liang
6. Qiankun Gao
7. Haoxuan Qu
8. Yanbei Chen
9. Yujin Tang
10. Dushyant Rao

Papers from James Smith's perspective

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

Their papers

• Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (2021) — 213 citations
• CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning (2022) — 609 citations
• A Closer Look at Rehearsal-Free Continual Learning * (2022) — 88 citations
• Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer (2021) — 49 citations
• Unsupervised Progressive Learning and the STAM Architecture (2019) — 41 citations
• Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies (2019) — 12 citations