Benjamin Charlier

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 30 citations into it
• Artificial Intelligence — 25 citations into it
• Model Architectures — 9 citations into it
• ML Systems & Hardware — 8 citations into it
• Optimization & Training — 8 citations into it; #1,651 of 2,601 by trust here, #2,081 by citations
• Graph Learning — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Benjamin Charlier trusts overall

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

1. Hubert Nguyen
2. Jeff Johnson
3. Sergey Ioffe
4. Matthijs Douze
5. G. Peyr'e
6. Herv'e J'egou
7. Matthias Fey
8. Thomas Kipf
9. Trevor J. Hastie
10. Maximilian Nickel

Who trusts Benjamin Charlier overall

1. Freyr Sverrisson
2. Jean Feydy
3. Bruno E. Correia
4. Ankit Gupta
5. Jonathan Berant
6. Rémi Flamary

Papers from Benjamin Charlier's perspective

Ordered by how strongly the researchers Benjamin Charlier 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Kernel Operations on the GPU, with Autodiff, without Memory Overflows (2020) — 222 citations
• Fast geometric learning with symbolic matrices (2020) — 49 citations