Chris G. Willcocks

Computer Vision and Pattern Recognition · 2018–2025

A researcher in the Science corpus: 5 papers; trusts 920 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 235 citations into it
• Artificial Intelligence — 190 citations into it
• Generative Models — 182 citations into it
• Probabilistic & Bayesian Methods — 62 citations into it
• Statistical and Nonlinear Physics — 53 citations into it
• Model Architectures — 36 citations into it
• Signal Processing — 34 citations into it
• Biophysics — 18 citations into it

Whom Chris G. Willcocks trusts overall

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

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

Who trusts Chris G. Willcocks overall

1. Julia Wolleb
2. Sam Bond-Taylor
3. Alexia Jolicoeur-Martineau
4. Andreas Blattmann

Papers from Chris G. Willcocks's perspective

Ordered by how strongly the researchers Chris G. Willcocks 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models (2021) — 220 citations
• Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models (2021) — 712 citations
• AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise (2022) — 484 citations
• A benchmark of expert-level academic questions to assess AI capabilities (2025) — 349 citations
• Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery (2018) — 329 citations