Chih-Kuan Yeh

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 12 papers; trusts 533 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 252 citations into it; #9,720 of 35,345 by trust here, #5,834 by citations
• Computer Vision and Pattern Recognition — 130 citations into it; #12,286 of 21,575 by trust here, #9,187 by citations
• Safety, Robustness & Interpretability — 103 citations into it
• Natural Language Processing — 55 citations into it
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Biophysics — 18 citations into it

Whom Chih-Kuan Yeh trusts overall

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

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

Who trusts Chih-Kuan Yeh overall

1. Sercan Ö. Arik
2. Julius Adebayo
3. Umang Bhatt
4. Weiwei Liu
5. Tomas Pfister
6. Andreas Madsen
7. Karthikeyan K
8. Meike Nauta
9. Pouya Pezeshkpour
10. Yuandong Tian

Papers from Chih-Kuan Yeh's perspective

Ordered by how strongly the researchers Chih-Kuan Yeh 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multi-label Zero-Shot Learning with Structured Knowledge Graphs (2017) — 307 citations
• Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023) — 990 citations
• Representer Point Selection for Explaining Deep Neural Networks (2018) — 313 citations
• Order-Free RNN with Visual Attention for Multi-Label Classification (2017) — 159 citations
• Learning Deep Latent Spaces for Multi-Label Classification (2017) — 145 citations
• Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching (2018) — 42 citations
• On Completeness-aware Concept-Based Explanations in Deep Neural Networks (2019) — 373 citations
• How Sensitive are Sensitivity-Based Explanations? (2019) — 23 citations
• On Concept-Based Explanations in Deep Neural Networks (2019) — 44 citations
• On the (In)fidelity and Sensitivity for Explanations. (2019) — 604 citations
• Learning Deep Latent Space for Multi-Label Classification (2017) — 160 citations
• Automatic Bridge Bidding Using Deep Reinforcement Learning (2016) — 46 citations