Bo Zhang
Computer Vision and Pattern Recognition · with Li · 2019–2023
A researcher in the Science corpus: 10 papers; trusts 1,019 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 327 citations into it; #12,880 of 21,575 by trust here, #4,690 by citations
- Artificial Intelligence — 238 citations into it; #19,437 of 35,345 by trust here, #8,077 by citations
- Model Architectures — 84 citations into it
- Generative Models — 79 citations into it; #1,034 of 1,956 by trust here, #1,651 by citations
- Neural Architecture Search & AutoML — 55 citations into it; #206 of 283 by trust here, #112 by citations
- Optimization & Training — 46 citations into it
- Efficiency & Compression — 41 citations into it
- Evaluation & Benchmarks — 33 citations into it
Whom Bo Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Diederik P. Kingma
- Geoffrey E. Hinton
- Quoc V. Le
- Yoshua Bengio
- Ilya Sutskever
- Ross B. Girshick
- Karen Simonyan
- Yann LeCun
Who trusts Bo Zhang overall
- Xiangxiang Chu
- Changlin Li
- Yujun Shen
- Houwen Peng
- Shan You
- Yong Guo
- Miao Zhang
- Yulin Wang
- Haokui Zhang
- Jiefeng Peng
Papers from Bo Zhang's perspective
Ordered by how strongly the researchers Bo 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Conditional Positional Encodings for Vision Transformers (2021) — 838 citations
- FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search (2019) — 370 citations
- Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (2019) — 287 citations
- YOLOv6 v3.0: A Full-Scale Reloading (2023) — 373 citations
- DARTS-: Robustly Stepping out of Performance Collapse Without Indicators (2020) — 194 citations
- Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search (2019) — 359 citations
- Disentangled Inference for GANs With Latently Invertible Autoencoder (2019) — 39 citations
- ScarletNAS: Bridging the Gap Between Scalability and Fairness in Neural Architecture Search (2019) — 27 citations
- LIA: Latently Invertible Autoencoder with Adversarial Learning (2019) — 21 citations
- SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search (2019) — 27 citations