Lynton Ardizzone
Computer Vision and Pattern Recognition · 2018–2020
A researcher in the Science corpus: 3 papers; trusts 167 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 65 citations into it
- Artificial Intelligence — 61 citations into it
- Generative Models — 55 citations into it
- Probabilistic & Bayesian Methods — 29 citations into it
- Statistical and Nonlinear Physics — 16 citations into it
- Statistics and Probability — 13 citations into it
- Signal Processing — 8 citations into it
- Model Architectures — 7 citations into it
Whom Lynton Ardizzone trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Laurent Dinh
- Alex Krizhevsky
- Karen Simonyan
- Max Welling
- Yann LeCun
- Kaiming He
- Radford M. Neal
Who trusts Lynton Ardizzone overall
- Denis A. Gudovskiy
- Andreas Lugmayr
- Robin Rombach
- Michael Dorkenwald
- Manoj Kumar
- Jingyun Liang
- Shun Ishizaka
- Hyungjin Chung
- Stefan T. Radev
- Jörn-Henrik Jacobsen
Papers from Lynton Ardizzone's perspective
Ordered by how strongly the researchers Lynton Ardizzone trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Guided Image Generation with Conditional Invertible Neural Networks (2019) — 351 citations
- Analyzing Inverse Problems with Invertible Neural Networks (2018) — 598 citations
- BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks (2020) — 297 citations