Georgia Atkinson

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 17 citations into it
• Ecology — 8 citations into it

Whom Georgia Atkinson trusts overall

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

1. Ben. G. Weinstein
2. Kaiming He
3. Ilya Loshchilov
4. Andrew Gilman

Papers from Georgia Atkinson's perspective

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

Their papers

• NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation (2020) — 33 citations
• The Northumberland Dolphin Dataset: A Multimedia Individual Cetacean Dataset for Fine-Grained Categorisation (2019)