Zheng Xu
Artificial Intelligence · with Goldstein · 2014–2023
A researcher in the Science corpus: 16 papers; trusts 1,008 researchers and is trusted by 61 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 913 citations into it; #5,644 of 35,345 by trust here, #1,759 by citations
- Privacy & Security — 291 citations into it
- Computer Vision and Pattern Recognition — 289 citations into it; #4,107 of 21,575 by trust here, #3,006 by citations
- Optimization & Training — 252 citations into it; #978 of 2,601 by trust here, #366 by citations
- Safety, Robustness & Interpretability — 181 citations into it
- Computer Networks and Communications — 79 citations into it
- Efficiency & Compression — 78 citations into it; #1,136 of 1,264 by trust here, #925 by citations
- Computational Mechanics — 75 citations into it
Whom Zheng Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Diederik P. Kingma
- Karen Simonyan
- Christian Szegedy
- Sergey Ioffe
Who trusts Zheng Xu overall
- Jianping Gou
- Gabriella Csurka
- Seyed Iman Mirzadeh
- Muhammad Ghifary
- Divya Saxena
- Wen Li
- Kaiyang Zhou
- Li Niu
- Ruishan Liu
- Chuang Gan
Papers from Zheng Xu's perspective
Ordered by how strongly the researchers Zheng Xu 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- 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)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Visualizing the Loss Landscape of Neural Nets (2017) — 2,377 citations
- Exploiting Low-Rank Structure from Latent Domains for Domain Generalization (2014) — 204 citations
- Domain Generalization and Adaptation Using Low Rank Exemplar SVMs (2018) — 125 citations
- Training Neural Networks Without Gradients: A Scalable ADMM Approach (2016) — 297 citations
- Advances and Open Problems in Federated Learning (2019) — 9,019 citations
- Training Quantized Nets: A Deeper Understanding (2017) — 230 citations
- Adaptive Consensus ADMM for Distributed Optimization (2017) — 73 citations
- Universal Adversarial Training (2018) — 211 citations
- How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy (2023) — 300 citations
- A Field Guide to Federated Optimization (2021) — 487 citations
- Practical and Private (Deep) Learning without Sampling or Shuffling (2021) — 253 citations
- Adversarial Training for Free! (2019) — 1,469 citations
- Training Student Networks for Acceleration with Conditional Adversarial Networks (2018) — 27 citations
- Group-driven Reinforcement Learning for Personalized mHealth Intervention (2017) — 20 citations
- Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks (2017) — 114 citations
- Stabilizing Adversarial Nets With Prediction Methods (2017) — 92 citations