Cheng-Yu Hsieh

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 6 papers; trusts 90 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it
• Safety, Robustness & Interpretability — 65 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it
• Natural Language Processing — 38 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Biophysics — 12 citations into it
• Reinforcement Learning — 10 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Cheng-Yu Hsieh trusts overall

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

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

Who trusts Cheng-Yu Hsieh overall

1. Umang Bhatt
2. Zirui Zhao
3. Yuandong Tian
4. Eric Zelikman
5. Guowei Xu
6. Rishabh Agarwal
7. Peiyi Wang
8. Xiaohan Xu
9. Julius Adebayo
10. Zheng Tian

Papers from Cheng-Yu Hsieh's perspective

Ordered by how strongly the researchers Cheng-Yu Hsieh 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023) — 990 citations
• A Survey on Programmatic Weak Supervision (2022) — 113 citations
• DataComp-LM: In search of the next generation of training sets for language models (2024) — 374 citations
• How Sensitive are Sensitivity-Based Explanations? (2019) — 23 citations
• On the (In)fidelity and Sensitivity for Explanations. (2019) — 604 citations
• Automatic Bridge Bidding Using Deep Reinforcement Learning (2016) — 46 citations