Kevin Roth

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 63 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #11,004 of 35,345 by trust here, #8,007 by citations
• Computer Vision and Pattern Recognition — 37 citations into it; #7,442 of 21,575 by trust here, #6,371 by citations
• Probabilistic & Bayesian Methods — 26 citations into it
• Optimization & Training — 17 citations into it; #1,106 of 2,601 by trust here, #924 by citations
• Generative Models — 16 citations into it; #710 of 1,956 by trust here, #776 by citations
• Model Architectures — 13 citations into it
• Statistical and Nonlinear Physics — 8 citations into it; #2,320 of 10,127 by trust here, #1,430 by citations
• Representation & Self-Supervised Learning — 5 citations into it

Whom Kevin Roth trusts overall

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

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

Who trusts Kevin Roth overall

1. Ngoc-Trung Tran
2. Paulina Grnarova
3. Lars M. Mescheder
4. Mohammad Rostami
5. Dan Zhang
6. Vaishnavh Nagarajan
7. G. Santhanam
8. Alberto Bietti
9. Karol Kurach
10. Stanislav Pidhorskyi

Papers from Kevin Roth's perspective

Ordered by how strongly the researchers Kevin Roth 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Stabilizing Training of Generative Adversarial Networks through Regularization (2017) — 481 citations
• Hydra: Preserving Ensemble Diversity for Model Distillation (2020) — 65 citations
• Appendix for: How Good is the Bayes Posterior in Deep Neural Networks Really? (2020) — 424 citations
• The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks (2020) — 55 citations