Nick Weston

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 443 researchers and is trusted by 233 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #2,720 of 21,575 by trust here, #3,786 by citations
• Artificial Intelligence — 68 citations into it; #5,408 of 35,345 by trust here, #5,565 by citations
• Model Architectures — 37 citations into it
• Optimization & Training — 36 citations into it
• Generative Models — 22 citations into it; #521 of 1,956 by trust here, #423 by citations
• Representation & Self-Supervised Learning — 9 citations into it
• Computational Mechanics — 7 citations into it; #4,179 of 10,001 by trust here, #1,711 by citations
• Aerospace Engineering — 5 citations into it

Whom Nick Weston trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Sergey Ioffe
7. Christian Szegedy
8. Ilya Sutskever
9. Yann LeCun
10. Jimmy Ba

Who trusts Nick Weston overall

1. Xuanyi Dong
2. Xiangxiang Chu
3. Ian J. Goodfellow
4. Andrew Brock
5. Purushotham Kamath
6. Jiahui Yu
7. Clemens Rosenbaum
8. Quoc V. Le
9. Sandro Skansi
10. Jun-Yan Zhu

Papers from Nick Weston's perspective

Ordered by how strongly the researchers Nick Weston 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Neural Photo Editing with Introspective Adversarial Networks (2016) — 477 citations
• Generative and Discriminative Voxel Modeling with Convolutional Neural Networks (2016) — 626 citations
• SMASH: One-Shot Model Architecture Search through HyperNetworks (2017) — 808 citations
• FreezeOut: Accelerate Training by Progressively Freezing Layers (2017) — 159 citations