Julien Diard

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 39 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Generative Models — 40 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it

Whom Julien Diard trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Graves
4. Yoshua Bengio
5. Radford M. Neal
6. Yann LeCun
7. Aäron van den Oord
8. Alex Krizhevsky
9. Ilya Sutskever
10. Sepp Hochreiter

Papers from Julien Diard's perspective

Ordered by how strongly the researchers Julien Diard 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. Learning Multiple Layers of Features from Tiny Images
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Dynamical Variational Autoencoders: A Comprehensive Review (2020) — 308 citations