Alexia Jolicoeur-Martineau

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 7 papers; trusts 656 researchers and is trusted by 326 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 155 citations into it; #2,130 of 21,575 by trust here, #3,929 by citations
• Generative Models — 135 citations into it; #121 of 1,956 by trust here, #491 by citations
• Artificial Intelligence — 113 citations into it
• Optimization & Training — 70 citations into it
• Statistical and Nonlinear Physics — 30 citations into it; #3,305 of 10,127 by trust here, #1,638 by citations
• Probabilistic & Bayesian Methods — 25 citations into it
• Computational Mechanics — 23 citations into it
• Signal Processing — 14 citations into it

Whom Alexia Jolicoeur-Martineau trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Martín Arjovsky
7. Jimmy Ba
8. Kaiming He
9. L. Eon Bottou
10. Yang Song

Who trusts Alexia Jolicoeur-Martineau overall

1. Saeed Anwar
2. Hongyu Liu
3. Yochai Blau
4. Valentin De Bortoli
5. Animesh Karnewar
6. Kai Zhang
7. Chenlin Meng
8. Zhihao Wang
9. Abdul Jabbar
10. Radu Timofte

Papers from Alexia Jolicoeur-Martineau's perspective

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

• The relativistic discriminator: a key element missing from standard GAN (2018) — 1,106 citations
• Adversarial score matching and improved sampling for image generation (2020) — 138 citations
• Gotta Go Fast When Generating Data with Score-Based Models (2021) — 265 citations
• MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation (2022) — 422 citations
• Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs (2019) — 12 citations
• Gradient penalty from a maximum margin perspective. (2020) — 11 citations
• Stochastic Hamiltonian Gradient Methods for Smooth Games (2020) — 55 citations