Francis Williams

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 139 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 112 citations into it
• Computational Mechanics — 72 citations into it
• Generative Models — 60 citations into it
• Artificial Intelligence — 57 citations into it
• Computer Graphics and Computer-Aided Design — 46 citations into it
• Geology — 16 citations into it
• Aerospace Engineering — 13 citations into it
• Optimization & Training — 13 citations into it

Whom Francis Williams trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Christian Szegedy
9. Radford M. Neal
10. Danilo Jimenez Rezende

Papers from Francis Williams's perspective

Ordered by how strongly the researchers Francis Williams 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• LION: Latent Point Diffusion Models for 3D Shape Generation (2022) — 720 citations
• Learning Smooth Neural Functions via Lipschitz Regularization (2022) — 135 citations
• ABC: A Big CAD Model Dataset for Geometric Deep Learning (2018) — 678 citations