Alexander Wong

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 712 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it; #6,255 of 21,575 by trust here, #6,326 by citations
• Artificial Intelligence — 87 citations into it; #17,476 of 35,345 by trust here, #18,186 by citations
• Radiology, Nuclear Medicine and Imaging — 39 citations into it; #479 of 3,265 by trust here, #825 by citations
• Evaluation & Benchmarks — 19 citations into it
• Safety, Robustness & Interpretability — 19 citations into it; #1,522 of 2,251 by trust here, #2,014 by citations
• Model Architectures — 18 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Natural Language Processing — 12 citations into it

Whom Alexander Wong trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Yann LeCun
8. Andrew Zisserman
9. Ross B. Girshick
10. Matthew D. Zeiler

Who trusts Alexander Wong overall

1. Muhammad Shoaib Farooq
2. Asmaa Abbas
3. Yujin Oh
4. Khalid El Asnaoui
5. Joseph Aylett-Bullock
6. Md. Zahangir Alom
7. Vinay Uday Prabhu
8. Quanshi Zhang
9. T. Ozturk
10. S. Rajaraman

Papers from Alexander Wong's perspective

Ordered by how strongly the researchers Alexander Wong 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets (2019) — 47 citations
• Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks (2017) — 63 citations
• Squeeze-and-Attention Networks for Semantic Segmentation (2019) — 287 citations
• COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020) — 2,747 citations
• Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video (2017) — 393 citations
• COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images (2020) — 588 citations
• Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction (2017) — 21 citations
• Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption (2017) — 28 citations
• Lung Nodule Classification Using Deep Features in CT Images (2015) — 330 citations