Hanjie Chen

Artificial Intelligence · 4 papers · 2019–2025

A researcher in the Science corpus: 4 papers; trusts 113 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 200 citations into it
• Natural Language Processing — 91 citations into it
• Safety, Robustness & Interpretability — 51 citations into it
• Computer Vision and Pattern Recognition — 43 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 15 citations into it
• Information Systems — 13 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Hanjie Chen trusts overall

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

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

Papers from Hanjie Chen's perspective

Ordered by how strongly the researchers Hanjie Chen 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. 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (2025) — 419 citations
• Explainability for Large Language Models: A Survey (2023) — 887 citations
• REV: Information-Theoretic Evaluation of Free-Text Rationales (2022) — 30 citations
• Improving the Interpretability of Neural Sentiment Classifiers via Data Augmentation (2019) — 7 citations