Florian Schmidt

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 354 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #12,919 of 35,345 by trust here, #27,551 by citations
• Natural Language Processing — 26 citations into it; #2,442 of 5,688 by trust here, #4,879 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #8,512 of 21,575 by trust here, #17,757 by citations
• Generative Models — 10 citations into it; #803 of 1,956 by trust here, #1,748 by citations
• Model Architectures — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Florian Schmidt trusts overall

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

1. Diederik P. Kingma
2. Ronald J. Williams
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Alex Krizhevsky
7. Ruslan Salakhutdinov
8. Tomas Mikolov
9. Jimmy Ba
10. Yann LeCun

Who trusts Florian Schmidt overall

1. Ting-Rui Chiang
2. Rui Liu
3. Yun-Nung Chen
4. Berrak Sisman
5. Patrick Esser
6. Kushal Arora
7. Tianxing He
8. Jingdong Li
9. Dongchao Yang
10. Nisan Stiennon

Papers from Florian Schmidt's perspective

Ordered by how strongly the researchers Florian Schmidt 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Generalization in Generation: A closer look at Exposure Bias (2019) — 152 citations