Tassilo Johannes Klein

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 220 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #4,753 of 35,345 by trust here, #4,231 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #6,586 of 21,575 by trust here, #5,464 by citations
• Natural Language Processing — 19 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 9 citations into it
• Radiology, Nuclear Medicine and Imaging — 8 citations into it; #2,128 of 3,265 by trust here, #2,400 by citations

Whom Tassilo Johannes Klein trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Ilya Sutskever
6. Diederik P. Kingma
7. Quoc V. Le
8. Kaiming He
9. Karen Simonyan
10. Jacob Devlin

Who trusts Tassilo Johannes Klein overall

1. Lichao Sun
2. Gyuhak Kim
3. Luping Wang
4. Guanglei Yang
5. Enrico Fini
6. Ali Ayub
7. Tribhuvanesh Orekondy
8. Congzheng Song
9. Stephanie L. Hyland
10. Tyler L. Hayes

Papers from Tassilo Johannes Klein's perspective

Ordered by how strongly the researchers Tassilo Johannes Klein 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning (2019) — 356 citations
• Differentially Private Federated Learning: A Client Level Perspective (2017) — 1,680 citations
• Contrastive Self-Supervised Learning for Commonsense Reasoning (2020) — 64 citations
• Learning to remember: Dynamic Generative Memory for Continual Learning (2018)
• DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy (2017) — 353 citations