Duen Horng Chau

Artificial Intelligence · 2007–2023

A researcher in the Science corpus: 14 papers; trusts 376 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 212 citations into it; #10,364 of 35,345 by trust here, #6,720 by citations
• Computer Vision and Pattern Recognition — 160 citations into it; #7,823 of 21,575 by trust here, #7,027 by citations
• Safety, Robustness & Interpretability — 95 citations into it
• Signal Processing — 34 citations into it; #3,803 of 7,572 by trust here, #5,570 by citations
• Natural Language Processing — 29 citations into it
• Information Systems — 25 citations into it; #2,085 of 8,771 by trust here, #2,604 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Biophysics — 14 citations into it; #983 of 1,196 by trust here, #754 by citations

Whom Duen Horng Chau trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Christian Szegedy
6. Ilya Sutskever
7. Yann LeCun
8. Diederik P. Kingma
9. Ian J. Goodfellow
10. Ross B. Girshick

Who trusts Duen Horng Chau overall

1. Binghui Wang
2. Daniele Ucci
3. Yanfang Ye
4. Neil Zhenqiang Gong
5. Ephraim Nissan
6. Fred Hohman
7. Leman Akoglu
8. Leonardo Aniello
9. Tao Li
10. Alex Beutel

Papers from Duen Horng Chau's perspective

Ordered by how strongly the researchers Duen Horng Chau trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector (2018) — 480 citations
• Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression (2017) — 353 citations
• DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models (2022) — 493 citations
• Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers (2018) — 606 citations
• ELF: An Early-Exiting Framework for Long-Tailed Classification (2020) — 30 citations
• ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models (2017) — 351 citations
• EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models (2021) — 29 citations
• LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked (2023) — 324 citations
• CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization (2020) — 318 citations
• RECAST: Interactive Auditing of Automatic Toxicity Detection Models (2020) — 7 citations
• Visual exploration of machine learning results using data cube analysis (2016) — 82 citations
• Guilt by association: large scale malware detection by mining file-relation graphs (2014) — 175 citations
• Polonium: Tera-Scale Graph Mining for Malware Detection (2013) — 55 citations
• Netprobe: a fast and scalable system for fraud detection in online auction networks (2007) — 469 citations