Cédric Archambeau

Artificial Intelligence · 2006–2022

A researcher in the Science corpus: 13 papers; trusts 671 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 224 citations into it; #2,975 of 35,345 by trust here, #8,810 by citations
• Computer Vision and Pattern Recognition — 145 citations into it; #11,660 of 21,575 by trust here, #13,542 by citations
• Meta & Continual Learning — 66 citations into it
• Representation & Self-Supervised Learning — 65 citations into it
• Optimization & Training — 52 citations into it
• Model Architectures — 29 citations into it
• Natural Language Processing — 29 citations into it
• Computational Theory and Mathematics — 27 citations into it; #431 of 12,182 by trust here, #2,060 by citations

Whom Cédric Archambeau trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Robert Tibshirani
5. Yoshua Bengio
6. Diederik P. Kingma
7. Kaiming He
8. Yann LeCun
9. Francis R. Bach
10. Michael I. Jordan

Who trusts Cédric Archambeau overall

1. Arto Klami
2. Matt P. Wand
3. Michalis K. Titsias
4. Francis R. Bach
5. David Rohde
6. Hilbert J. Kappen
7. Yarin Gal
8. David M. Blei
9. Miguel L'azaro-Gredilla
10. James Hensman

Papers from Cédric Archambeau's perspective

Ordered by how strongly the researchers Cédric Archambeau 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The Variational Gaussian Approximation Revisited (2009) — 410 citations
• Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning (2019) — 118 citations
• Memory Efficient Continual Learning with Transformers (2022) — 70 citations
• Continual Learning with Transformers for Image Classification (2022) — 27 citations
• Memory Efficient Continual Learning for Neural Text Classification (2022) — 5 citations
• LEEP: A New Measure to Evaluate Transferability of Learned Representations (2020) — 295 citations
• Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start (2017) — 25 citations
• Bayesian Optimization with Tree-structured Dependencies (2017) — 58 citations
• Sparse Bayesian Multi-Task Learning (2011) — 52 citations
• Sparse probabilistic projections (2008) — 150 citations
• Gaussian Process Approximations of Stochastic Differential Equations (2007) — 176 citations
• Variational Inference for Diffusion Processes (2007) — 106 citations
• Template Attacks in Principal Subspaces (2006) — 301 citations