Magda Gregorová

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 225 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it; #7,721 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #5,615 of 21,575 by trust here, #9,435 by citations
• Generative Models — 32 citations into it; #636 of 1,956 by trust here, #1,221 by citations
• Probabilistic & Bayesian Methods — 25 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 15 citations into it
• Optimization & Training — 12 citations into it
• Meta & Continual Learning — 9 citations into it; #269 of 1,041 by trust here, #583 by citations

Whom Magda Gregorová trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Radford M. Neal
7. L. Eon Bottou
8. Max Welling
9. Ilya Sutskever
10. Kaiming He

Who trusts Magda Gregorová overall

1. Fei Ye
2. Adrian G. Bors
3. Rahaf Aljundi
4. Matthias De Lange
5. Blake Camp
6. Jaya Krishna Mandivarapu
7. Rui Gao
8. Mustafa Burak Gurbuz
9. Timothée Lesort
10. Qingfeng Lan

Papers from Magda Gregorová's perspective

Ordered by how strongly the researchers Magda Gregorová 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Lifelong Generative Modeling (2017) — 134 citations
• Continual Classification Learning Using Generative Models (2018) — 57 citations