Scott Grigsby

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it
• Meta & Continual Learning — 19 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Reinforcement Learning — 9 citations into it

Whom Scott Grigsby trusts overall

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

1. Bing Liu
2. Zhiyuan Chen
3. Sahisnu Mazumder

Papers from Scott Grigsby's perspective

Ordered by how strongly the researchers Scott Grigsby 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• AI Autonomy: Self-initiated Open-world Continual Learning and Adaptation (2022) — 49 citations
• Self-Initiated Open World Learning for Autonomous AI Agents (2021) — 11 citations