Theodore Lim

Computer Vision and Pattern Recognition · with Brock · 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; #2,719 of 21,575 by trust here, #3,786 by citations
• Artificial Intelligence — 68 citations into it; #5,407 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; #520 of 1,956 by trust here, #423 by citations
• Representation & Self-Supervised Learning — 9 citations into it
• Computational Mechanics — 7 citations into it; #4,178 of 10,001 by trust here, #1,711 by citations
• Aerospace Engineering — 5 citations into it

Whom Theodore Lim trusts overall

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

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

Who trusts Theodore Lim overall

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

Papers from Theodore Lim's perspective

Ordered by how strongly the researchers Theodore Lim 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

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