Sarah Parisot

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 11 papers; trusts 432 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 228 citations into it; #11,420 of 35,345 by trust here, #2,892 by citations
• Computer Vision and Pattern Recognition — 191 citations into it; #7,892 of 21,575 by trust here, #2,355 by citations
• Cognitive Neuroscience — 102 citations into it
• Radiology, Nuclear Medicine and Imaging — 86 citations into it
• Meta & Continual Learning — 77 citations into it; #275 of 1,041 by trust here, #28 by citations
• Representation & Self-Supervised Learning — 67 citations into it
• Optimization & Training — 48 citations into it
• Graph Learning — 40 citations into it; #446 of 693 by trust here, #315 by citations

Whom Sarah Parisot trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Max Welling
9. Karen Simonyan
10. Thomas Kipf

Who trusts Sarah Parisot overall

1. Gyuhak Kim
2. Zixuan Ke
3. Xiao-Meng Zhang
4. Nathanael Perraudin
5. Federico Monti
6. Marc Masana
7. Yaoyao Liu
8. Michael M. Bronstein
9. Da-Wei Zhou
10. Sofia Ira Ktena

Papers from Sarah Parisot's perspective

Ordered by how strongly the researchers Sarah Parisot 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Continual Learning Survey: Defying Forgetting in Classification Tasks (2019) — 2,435 citations
• Learning Conditioned Graph Structures for Interpretable Visual Question Answering (2018) — 257 citations
• More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning (2020) — 115 citations
• Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks (2017) — 216 citations
• Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem (2020) — 26 citations
• Continual learning: A comparative study on how to defy forgetting in classification tasks (2019) — 260 citations
• Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease (2018) — 743 citations
• Metric learning with spectral graph convolutions on brain connectivity networks (2018) — 375 citations
• Spectral Graph Convolutions for Population-based Disease Prediction (2017) — 250 citations
• Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017) — 350 citations
• DeepMedic for Brain Tumor Segmentation (2016) — 325 citations