Mélanie Ducoffe

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 139 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #24,256 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 65 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Model Architectures — 24 citations into it
• Natural Language Processing — 20 citations into it
• Control and Systems Engineering — 18 citations into it
• Signal Processing — 12 citations into it
• Safety, Robustness & Interpretability — 10 citations into it

Whom Mélanie Ducoffe trusts overall

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

1. Nicholas Carlini
2. Geoffrey E. Hinton
3. Christian Szegedy
4. Ian J. Goodfellow
5. David A. Wagner
6. Alex Krizhevsky
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. Alex Graves

Who trusts Mélanie Ducoffe overall

1. Shiqi Wang
2. J. Ash
3. Pengzhen Ren
4. Yizheng Chen
5. Chicheng Zhang
6. Yun Xiao
7. Kailas Vodrahalli
8. Ahmed Abdou

Papers from Mélanie Ducoffe's perspective

Ordered by how strongly the researchers Mélanie Ducoffe 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. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Adversarial Active Learning for Deep Networks: a Margin Based Approach (2018) — 328 citations
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020) — 488 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations