Jasmine Collins

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 3 papers; trusts 122 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it
• Artificial Intelligence — 46 citations into it; #35,196 of 35,345 by trust here, #19,873 by citations
• Cognitive Neuroscience — 23 citations into it
• Model Architectures — 20 citations into it; #2,263 of 2,278 by trust here, #1,693 by citations
• Optimization & Training — 9 citations into it
• Electrical and Electronic Engineering — 7 citations into it
• Natural Language Processing — 7 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Jasmine Collins trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Diederik P. Kingma
7. J. Urgen Schmidhuber
8. Sepp Hochreiter
9. Ross B. Girshick
10. Yann LeCun

Who trusts Jasmine Collins overall

1. Hieu Pham
2. Yuqiao Liu
3. Jakob N. Foerster
4. D. Golovin
5. Kai Zhang
6. Gábor Melis
7. Yanan Sun
8. Justin Gilmer
9. Melody Y. Guan
10. Yicong Hong

Papers from Jasmine Collins's perspective

Ordered by how strongly the researchers Jasmine Collins 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Capacity and Trainability in Recurrent Neural Networks (2016) — 215 citations
• ABO: Dataset and Benchmarks for Real-World 3D Object Understanding (2021) — 412 citations
• Inferring single-trial neural population dynamics using sequential auto-encoders (2017) — 800 citations