Yikang Shen

Artificial Intelligence · with Gan · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 97 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 197 citations into it
• Computer Vision and Pattern Recognition — 115 citations into it
• Model Architectures — 25 citations into it
• Reinforcement Learning — 15 citations into it

Whom Yikang Shen trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Alex Graves
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Alec Radford
8. Kaiming He
9. Yann LeCun
10. Richard S. Sutton

Papers from Yikang Shen's perspective

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

Their papers

• Long Range Arena: A Benchmark for Efficient Transformers (2020) — 917 citations
• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision (2023) — 457 citations
• Aligning Large Multimodal Models with Factually Augmented RLHF (2023) — 811 citations
• Planning with Large Language Models for Code Generation (2023) — 256 citations
• See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning (2023) — 57 citations