J. Hong

Artificial Intelligence · with Wang · 2023

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it
• Reinforcement Learning — 13 citations into it

Whom J. Hong trusts overall

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

1. David R Ha
2. J. Urgen Schmidhuber

Papers from J. Hong's perspective

Ordered by how strongly the researchers J. Hong 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Chain of Thought Prompting Elicits Reasoning in Large Language Models (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Reasoning with Language Model is Planning with World Model (2023) — 1,077 citations