Stéphane Canu

Artificial Intelligence · 2004–2019

A researcher in the Science corpus: 13 papers; trusts 304 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #6,140 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #5,013 of 21,575 by trust here, #7,657 by citations
• Statistics and Probability — 61 citations into it
• Computational Mechanics — 44 citations into it; #2,867 of 10,001 by trust here, #2,942 by citations
• Statistical Learning Theory — 43 citations into it
• Probabilistic & Bayesian Methods — 30 citations into it
• Optimization & Training — 24 citations into it; #1,011 of 2,601 by trust here, #1,537 by citations
• Representation & Self-Supervised Learning — 23 citations into it

Whom Stéphane Canu trusts overall

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

1. Bernhard Schölkopf
2. Alex Smola
3. Yann LeCun
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alex Krizhevsky
7. Kaiming He
8. Quoc V. Le
9. Diederik P. Kingma
10. Grace Wahba

Who trusts Stéphane Canu overall

1. Bo Dai
2. Yoshua Bengio
3. Michael Arbel
4. Yang Song
5. Arthur Gretton
6. Siqi Sun
7. Naoya Takeishi
8. Francis R. Bach
9. Wenliang Li
10. Julien Mairal

Papers from Stéphane Canu's perspective

Ordered by how strongly the researchers Stéphane Canu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• More efficiency in multiple kernel learning (2007) — 326 citations
• Heteroscedastic Gaussian process regression (2005) — 246 citations
• Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming (2009) — 329 citations
• Kernel methods and the exponential family (2006) — 111 citations
• Operator-valued Kernels for Learning from Functional Response Data (2015) — 154 citations
• A review: Deep learning for medical image segmentation using multi-modality fusion (2019) — 606 citations
• Support Measure Data Description A One-Class Classifier for Group Anomaly Detection (2017)
• Support Measure Data Description for group anomaly detection (2015) — 15 citations
• Akaike's Information Criterion, Cp and Estimators of Loss for Elliptically Symmetric Distributions (2014) — 50 citations
• $\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning (2011) — 94 citations
• Nonlinear functional regression: a functional RKHS approach (2010) — 62 citations
• Training Invariant Support Vector Machines using Selective Sampling (2005)
• Learning with non-positive kernels (2004) — 296 citations