Christopher Beckham

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 50 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it
• Optimization & Training — 26 citations into it
• Representation & Self-Supervised Learning — 22 citations into it; #2,686 of 3,145 by trust here, #1,081 by citations
• Safety, Robustness & Interpretability — 15 citations into it
• Model Architectures — 11 citations into it

Whom Christopher Beckham trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Christian Szegedy
6. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. Ian J. Goodfellow
10. Ruslan Salakhutdinov

Who trusts Christopher Beckham overall

1. Linjun Zhang
2. Sang-Hoon Lee
3. Vikas Verma
4. Da-Wei Zhou
5. Ning Yu
6. Hongyu Guo
7. Jang-Hyun Kim
8. Sangdoo Yun
9. Zhun Deng
10. Kenji Kawaguchi

Papers from Christopher Beckham's perspective

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

Their papers

• On Adversarial Mixup Resynthesis (2019) — 75 citations
• Manifold Mixup: Better Representations by Interpolating Hidden States (2018) — 1,613 citations
• Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer (2018) — 67 citations