Zhihua Zhang
Artificial Intelligence · with Li · 2016–2020
A researcher in the Science corpus: 7 papers; trusts 191 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 180 citations into it; #16,392 of 35,345 by trust here, #7,728 by citations
- Optimization & Training — 156 citations into it; #1,641 of 2,601 by trust here, #1,841 by citations
- Federated & Distributed Learning — 60 citations into it; #130 of 319 by trust here, #76 by citations
- Computational Mechanics — 59 citations into it
- Computer Vision and Pattern Recognition — 56 citations into it
- Computer Networks and Communications — 34 citations into it; #3,943 of 9,886 by trust here, #1,003 by citations
- Computational Theory and Mathematics — 31 citations into it
- ML Systems & Hardware — 26 citations into it
Whom Zhihua Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yuchen Zhang
- Yurii E. Nesterov
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Kaiming He
- Michael W. Mahoney
- Ohad Shamir
- L. Eon Bottou
Who trusts Zhihua Zhang overall
Papers from Zhihua Zhang's perspective
Ordered by how strongly the researchers Zhihua Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On the Convergence of FedAvg on Non-IID Data (2019) — 3,042 citations
- Communication Efficient Decentralized Training with Multiple Local Updates (2019) — 56 citations
- An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization (2020) — 8 citations
- Do Subsampled Newton Methods Work for High-Dimensional Data? (2019) — 16 citations
- Communication-Efficient Local Decentralized SGD Methods (2019) — 76 citations
- Approximate Newton Methods and Their Local Convergence (2017) — 26 citations
- Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features (2016) — 3 citations