Sindy Löwe

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 234 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #17,572 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Representation & Self-Supervised Learning — 29 citations into it; #1,846 of 3,145 by trust here, #2,624 by citations
• Optimization & Training — 23 citations into it
• Cognitive Neuroscience — 18 citations into it
• Model Architectures — 16 citations into it
• Generative Models — 10 citations into it
• Signal Processing — 10 citations into it; #3,570 of 7,572 by trust here, #4,900 by citations

Whom Sindy Löwe trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Aapo Hyvärinen
4. Yoshua Bengio
5. Alex Krizhevsky
6. Diederik P. Kingma
7. J. Urgen Schmidhuber
8. Ralph Linsker
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Sindy Löwe overall

1. Geoffrey E. Hinton
2. Morgane Rivière
3. Paul Bergmann
4. Phúc H. Lê Khắc
5. Emmanuel Dupoux
6. Evgeny Kharitonov
7. Kilian Batzner
8. Yoshua Bengio
9. Hanqiu Deng
10. Graham F. Healy

Papers from Sindy Löwe's perspective

Ordered by how strongly the researchers Sindy Löwe 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Putting An End to End-to-End: Gradient-Isolated Learning of Representations (2019) — 168 citations
• Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders (2018) — 825 citations
• Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning (2019) — 5 citations