Tsui-Wei Weng

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 66 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #9,496 of 35,345 by trust here, #6,664 by citations
• Safety, Robustness & Interpretability — 94 citations into it; #1,278 of 2,251 by trust here, #743 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #18,337 of 21,575 by trust here, #12,419 by citations
• Signal Processing — 20 citations into it
• Hardware and Architecture — 13 citations into it
• Electrical and Electronic Engineering — 6 citations into it; #3,853 of 16,539 by trust here, #4,967 by citations
• Program Analysis & Verification — 6 citations into it; #296 of 301 by trust here, #60 by citations

Whom Tsui-Wei Weng trusts overall

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

1. Nicholas Carlini
2. Ian J. Goodfellow
3. Christian Szegedy
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Nicolas Papernot
7. Alexey Kurakin
8. Kaiming He
9. Yoshua Bengio
10. David A. Wagner

Who trusts Tsui-Wei Weng overall

1. Huan Zhang
2. Matt Jordan
3. Shiqi Wang
4. Klas Leino
5. Jeremy M. Cohen
6. Shubham Sharma
7. Sungyoon Lee
8. Ian J. Goodfellow
9. Hadi Salman
10. Mahyar Fazlyab

Papers from Tsui-Wei Weng's perspective

Ordered by how strongly the researchers Tsui-Wei Weng 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach (2018) — 524 citations
• Towards Fast Computation of Certified Robustness for ReLU Networks (2018) — 755 citations
• Efficient Neural Network Robustness Certification with General Activation Functions (2018) — 936 citations
• Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective (2019) — 538 citations
• Label-Free Concept Bottleneck Models (2023) — 334 citations