Zhen Dong

Computer Vision and Pattern Recognition · with Keutzer · 2019–2023

A researcher in the Science corpus: 9 papers; trusts 479 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 348 citations into it; #5,070 of 21,575 by trust here, #2,983 by citations
• Artificial Intelligence — 309 citations into it; #6,333 of 35,345 by trust here, #3,842 by citations
• Efficiency & Compression — 237 citations into it; #284 of 1,264 by trust here, #222 by citations
• Model Architectures — 78 citations into it
• Optimization & Training — 47 citations into it
• Electrical and Electronic Engineering — 41 citations into it
• Generative Models — 32 citations into it
• Statistical and Nonlinear Physics — 25 citations into it

Whom Zhen Dong trusts overall

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

1. Yoshua Bengio
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Christian Szegedy
7. Ross B. Girshick
8. Diederik P. Kingma
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Zhen Dong overall

1. Zi-Mei Yao
2. Sehoon Kim
3. A. Gholami
4. Yaohui Cai
5. Kurt Keutzer
6. Michael W. Mahoney
7. Jerry Chee
8. Sein Park
9. Markus Nagel
10. Mart van Baalen

Papers from Zhen Dong's perspective

Ordered by how strongly the researchers Zhen Dong trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT (2019) — 675 citations
• ZeroQ: A Novel Zero Shot Quantization Framework (2020) — 496 citations
• HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision (2019) — 686 citations
• A Survey of Quantization Methods for Efficient Neural Network Inference (2021) — 1,633 citations
• HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks (2019) — 394 citations
• SqueezeLLM: Dense-and-Sparse Quantization (2023) — 345 citations
• Q-Diffusion: Quantizing Diffusion Models (2023) — 316 citations
• CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs (2021) — 62 citations
• HAWQV3: Dyadic Neural Network Quantization (2020) — 93 citations