Marta Garnelo

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 11 papers; trusts 442 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it; #6,483 of 35,345 by trust here, #3,859 by citations
• Computer Vision and Pattern Recognition — 154 citations into it; #16,345 of 21,575 by trust here, #4,658 by citations
• Generative Models — 82 citations into it
• Representation & Self-Supervised Learning — 63 citations into it; #2,177 of 3,145 by trust here, #2,587 by citations
• Reinforcement Learning — 49 citations into it; #1,152 of 2,242 by trust here, #1,092 by citations
• Probabilistic & Bayesian Methods — 35 citations into it; #1,374 of 1,525 by trust here, #351 by citations
• Management Science and Operations Research — 17 citations into it
• Model Architectures — 15 citations into it; #1,887 of 2,278 by trust here, #762 by citations

Whom Marta Garnelo trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Alex Graves
6. J. Urgen Schmidhuber
7. Alex Krizhevsky
8. Ilya Sutskever
9. Oriol Vinyals
10. Kaiming He

Who trusts Marta Garnelo overall

1. Vincent Sitzmann
2. Blai Bonet
3. Jonathan Gordon
4. Alejandro Barredo Arrieta
5. Eric Crawford
6. Luisa M. Zintgraf
7. R'obert Csord'as
8. Sjoerd van Steenkiste
9. Juho Lee
10. Mingzhang Yin

Papers from Marta Garnelo's perspective

Ordered by how strongly the researchers Marta Garnelo 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Neural scene representation and rendering (2018) — 674 citations
• Towards Deep Symbolic Reinforcement Learning (2016) — 236 citations
• Conditional Neural Processes (2018) — 864 citations
• Open-ended Learning in Symmetric Zero-sum Games (2019) — 201 citations
• Neural Processes (2018) — 617 citations
• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (2016) — 734 citations
• Attentive Neural Processes (2019) — 540 citations
• Reconciling deep learning with symbolic artificial intelligence: representing objects and relations (2019) — 237 citations
• Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets (2020) — 7 citations
• Adaptive Posterior Learning: few-shot learning with a surprise-based memory module (2019) — 83 citations
• Consistent Generative Query Networks (2018) — 42 citations