Jason Zhi Liang

Artificial Intelligence · 2015–2024

A researcher in the Science corpus: 5 papers; trusts 193 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 128 citations into it; #11,628 of 35,345 by trust here, #6,014 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Model Architectures — 43 citations into it
• Natural Language Processing — 27 citations into it
• Optimization & Training — 23 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Neural Architecture Search & AutoML — 11 citations into it

Whom Jason Zhi Liang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Alex Krizhevsky
6. Frank Hutter
7. Faustino J. Gomez
8. Ilya Sutskever
9. Yann LeCun
10. Risto Miikkulainen

Who trusts Jason Zhi Liang overall

1. Andrés Camero
2. Michael Crawshaw
3. Simon Vandenhende
4. Enrique Alba
5. Jamal Toutouh
6. Ferran Alet
7. Yuqiao Liu
8. Sebastian Ruder
9. Yuan Gao
10. Krzysztof Maziarz

Papers from Jason Zhi Liang's perspective

Ordered by how strongly the researchers Jason Zhi Liang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Evolving Deep Neural Networks (2017) — 962 citations
• Evolutionary architecture search for deep multitask networks (2018) — 128 citations
• Evolutionary Bilevel Optimization for Complex Control Tasks (2015) — 29 citations
• PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation (2024) — 1,268 citations
• Evolutionary neural AutoML for deep learning (2019) — 120 citations