Simon See

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 3 papers; trusts 100 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it
• Artificial Intelligence — 87 citations into it
• Model Architectures — 23 citations into it

Whom Simon See trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Ross B. Girshick
7. Christian Szegedy
8. X. Zhang
9. Ilya Sutskever
10. Shaoqing Ren

Papers from Simon See's perspective

Ordered by how strongly the researchers Simon See 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. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
4. Learning Multiple Layers of Features from Tiny Images
5. Going deeper with convolutions
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. ImageNet: A large-scale hierarchical image database
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (2018) — 29 citations
• Continual Semantic Segmentation with Automatic Memory Sample Selection (2023) — 81 citations
• DeepHunter: a coverage-guided fuzz testing framework for deep neural networks (2019) — 472 citations