Kristjan H. Greenewald

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 267 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it; #23,721 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 68 citations into it
• Evaluation & Benchmarks — 30 citations into it
• Scaling & Learning Theory — 17 citations into it
• Efficiency & Compression — 13 citations into it
• Model Architectures — 11 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Kristjan H. Greenewald trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. David G. Lowe
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Ross B. Girshick
10. Alex Graves

Who trusts Kristjan H. Greenewald overall

1. Qinbin Li
2. Hongyi Wang
3. Yaron Lipman

Papers from Kristjan H. Greenewald's perspective

Ordered by how strongly the researchers Kristjan H. Greenewald 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• The Computational Limits of Deep Learning (2020) — 707 citations
• Bayesian Nonparametric Federated Learning of Neural Networks (2019) — 912 citations
• Statistical Model Aggregation via Parameter Matching (2019) — 38 citations
• Estimating Information Flow in Neural Networks (2018) — 32 citations