Cian Eastwood

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 227 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 36 citations into it; #17,130 of 21,575 by trust here, #20,756 by citations
• Artificial Intelligence — 26 citations into it; #28,026 of 35,345 by trust here, #33,294 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #591 of 3,145 by trust here, #1,671 by citations
• Generative Models — 17 citations into it
• Optimization & Training — 5 citations into it

Whom Cian Eastwood trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Jimmy Ba
5. Kaiming He
6. Alex Krizhevsky
7. Yann LeCun
8. Ilya Sutskever
9. Aaron C. Courville
10. Radford M. Neal

Who trusts Cian Eastwood overall

1. Francesco Locatello
2. Stefan Bauer
3. Bernhard Schölkopf
4. Raphael Suter
5. Sjoerd van Steenkiste
6. Klaus Greff
7. Drew A. Hudson
8. Zongze Wu
9. Olivier Bachem
10. Frederik Trauble

Papers from Cian Eastwood's perspective

Ordered by how strongly the researchers Cian Eastwood 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views (2021) — 62 citations
• A Framework for the Quantitative Evaluation of Disentangled Representations (2018) — 556 citations