Tom Fletcher

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 123 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 41 citations into it
• Artificial Intelligence — 40 citations into it
• Generative Models — 16 citations into it; #1,612 of 1,956 by trust here, #1,651 by citations
• Representation & Self-Supervised Learning — 15 citations into it; #2,609 of 3,145 by trust here, #2,488 by citations
• Probabilistic & Bayesian Methods — 12 citations into it
• Statistical Learning Theory — 10 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 7 citations into it

Whom Tom Fletcher trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Yann LeCun
5. Alex Krizhevsky
6. Ian J. Goodfellow
7. Christopher K. I. Williams
8. Kaiming He
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Tom Fletcher overall

1. Jie Tang
2. Ming Ding
3. Jie Zhang

Papers from Tom Fletcher's perspective

Ordered by how strongly the researchers Tom Fletcher trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Latent Space Non-Linear Statistics (2018) — 35 citations
• Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference (2017) — 161 citations
• Local Group Invariant Representations via Orbit Embeddings (2016) — 40 citations