Julien Cornebise

Artificial Intelligence · with Blundell · 2008–2020

A researcher in the Science corpus: 5 papers; trusts 47 researchers and is trusted by 221 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #1,733 of 35,345 by trust here, #3,091 by citations
• Probabilistic & Bayesian Methods — 41 citations into it; #104 of 1,525 by trust here, #79 by citations
• Statistics and Probability — 30 citations into it; #11,280 of 12,932 by trust here, #7,914 by citations
• Optimization & Training — 15 citations into it
• Model Architectures — 11 citations into it
• Management Science and Operations Research — 8 citations into it; #309 of 11,946 by trust here, #244 by citations
• Computer Vision and Pattern Recognition — 7 citations into it
• Generative Models — 6 citations into it

Whom Julien Cornebise trusts overall

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

1. Arnaud Doucet
2. Yann LeCun
3. Geoffrey E. Hinton
4. Haikady N. Nagaraja
5. Olivier Capp'e
6. Alex Graves
7. David John Cameron MacKay
8. Rudolph van der Merwe
9. Jun S. Liu
10. Reuven Y. Rubinstein

Who trusts Julien Cornebise overall

1. Yarin Gal
2. Chunyuan Li
3. Christos Louizos
4. Dustin Tran
5. Max Welling
6. Sebastian Farquhar
7. Diederik P. Kingma
8. Jiaxin Shi
9. Hao Wang
10. Chen Zeno

Papers from Julien Cornebise's perspective

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

Their papers

• Weight Uncertainty in Neural Networks (2015) — 2,165 citations
• AI for social good: unlocking the opportunity for positive impact (2020) — 296 citations
• Adaptive methods for sequential importance sampling with application to state space models (2008) — 91 citations
• Weight Uncertainty in Neural Network (2015) — 2,425 citations
• Adaptive sequential Monte Carlo by means of mixture of experts (2011) — 11 citations