Kuan-Chieh Jackson Wang

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 123 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #29,928 of 35,345 by trust here, #11,921 by citations
• Computer Vision and Pattern Recognition — 79 citations into it; #18,379 of 21,575 by trust here, #8,296 by citations
• Probabilistic & Bayesian Methods — 61 citations into it; #1,135 of 1,525 by trust here, #441 by citations
• Generative Models — 48 citations into it; #1,686 of 1,956 by trust here, #1,748 by citations
• Safety, Robustness & Interpretability — 29 citations into it
• Optimization & Training — 18 citations into it
• Signal Processing — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it

Whom Kuan-Chieh Jackson Wang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Max Welling
5. Yoshua Bengio
6. Alex Graves
7. Yann LeCun
8. Radford M. Neal
9. Ilya Sutskever
10. Kaiming He

Who trusts Kuan-Chieh Jackson Wang overall

1. Yilun Du
2. Neale Ratzlaff
3. Geoffrey E. Hinton
4. Sam Bond-Taylor
5. Laurent Valentin Jospin
6. Christian Henning
7. Josh Tenenbaum
8. Erik A. Daxberger
9. Prafulla Dhariwal
10. Weili Nie

Papers from Kuan-Chieh Jackson Wang's perspective

Ordered by how strongly the researchers Kuan-Chieh Jackson Wang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One (2019) — 664 citations
• Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling (2020) — 100 citations
• UvA-DARE (Digital Academic Repository) Neural Relational Inference for Interacting Systems Neural Relational Inference for Interacting Systems (2018)
• Adversarial Distillation of Bayesian Neural Network Posteriors (2018) — 61 citations