Jingzhao Zhang
Artificial Intelligence · with Sra · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it
- Optimization & Training — 62 citations into it
- Computational Mechanics — 45 citations into it
- Management Science and Operations Research — 17 citations into it
- Natural Language Processing — 11 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
Whom Jingzhao Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- John C. Duchi
- Geoffrey E. Hinton
- Yoshua Bengio
- Jimmy Ba
- Kaiming He
- Yurii E. Nesterov
- Elad Hazan
- Sepp Hochreiter
Who trusts Jingzhao Zhang overall
- Hong Liu
- Sai Praneeth Karimireddy
- Zhicheng Huang
- Xiangning Chen
- Zhiyuan Li
- Boris Knyazev
- Zhao Chen
- Liyuan Liu
- Zhaoyang Zeng
- Martin Jaggi
Papers from Jingzhao Zhang's perspective
Ordered by how strongly the researchers Jingzhao Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity (2019) — 627 citations
- Why ADAM Beats SGD for Attention Models (2019) — 88 citations
- Gate-tunable room-temperature ferromagnetism in two-dimensional Fe3GeTe2 (2018) — 2,261 citations
- Why are Adaptive Methods Good for Attention Models? (2020) — 381 citations