Emile Mathieu

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 107 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 62 citations into it
• Generative Models — 59 citations into it
• Artificial Intelligence — 58 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Probabilistic & Bayesian Methods — 26 citations into it
• Statistical and Nonlinear Physics — 21 citations into it
• Signal Processing — 8 citations into it
• Biophysics — 7 citations into it

Whom Emile Mathieu trusts overall

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

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

Who trusts Emile Mathieu overall

1. Fei Ye
2. Adrian G. Bors
3. Gabriele Corso
4. Namrata Anand
5. Bowen Jing
6. Francesco Locatello

Papers from Emile Mathieu's perspective

Ordered by how strongly the researchers Emile Mathieu 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022) — 200 citations
• SE(3) diffusion model with application to protein backbone generation (2023) — 343 citations
• Riemannian Score-Based Generative Modeling (2022) — 278 citations
• Disentangling Disentanglement in Variational Autoencoders (2018) — 319 citations