Alina Jade Barnett

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 533 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 104 citations into it; #7,994 of 21,575 by trust here, #6,873 by citations
• Artificial Intelligence — 98 citations into it; #12,439 of 35,345 by trust here, #10,319 by citations
• Safety, Robustness & Interpretability — 26 citations into it; #760 of 2,251 by trust here, #849 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 12 citations into it
• Biophysics — 10 citations into it
• Natural Language Processing — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Alina Jade Barnett trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Alex Krizhevsky
4. Kaiming He
5. Andrew Zisserman
6. Yoshua Bengio
7. Diederik P. Kingma
8. David G. Lowe
9. Ross B. Girshick
10. Yann LeCun

Who trusts Alina Jade Barnett overall

1. Cynthia Rudin
2. Peter Hase
3. Meike Nauta
4. Mark T. Keane
5. Wieland Brendel
6. Riccardo Guidotti
7. Sercan Ö. Arik
8. José Jiménez-Luna
9. Yitao Peng
10. Bas H. M. van der Velden

Papers from Alina Jade Barnett's perspective

Ordered by how strongly the researchers Alina Jade Barnett 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• This looks like that: deep learning for interpretable image recognition (2018) — 1,568 citations
• A case-based interpretable deep learning model for classification of mass lesions in digital mammography (2021) — 193 citations
• This Looks Like That: Deep Learning for Interpretable Image Recognition (2018)