Franccois Petitjean

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 6 papers; trusts 175 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #24,602 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Signal Processing — 44 citations into it; #2,436 of 7,572 by trust here, #2,589 by citations
• Media Technology — 23 citations into it; #2,102 of 3,621 by trust here, #3,091 by citations
• Model Architectures — 18 citations into it
• Ecology — 17 citations into it; #4,864 of 10,717 by trust here, #9,634 by citations
• Optimization & Training — 15 citations into it
• Management Science and Operations Research — 13 citations into it; #7,791 of 11,946 by trust here, #4,118 by citations

Whom Franccois Petitjean trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Ilya Sutskever
7. Kaiming He
8. Ali Rahimi
9. Jimmy Ba
10. Toni M. Rath

Who trusts Franccois Petitjean overall

1. John Paparrizos
2. Marco Cuturi
3. Brian Kenji Iwana
4. Devis Tuia
5. Charles F. Jekel
6. Luis Gravano
7. Mathieu Blondel
8. Seiichi Uchida
9. Angus Dempster
10. Claudio Persello

Papers from Franccois Petitjean's perspective

Ordered by how strongly the researchers Franccois Petitjean 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. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Characterizing concept drift (2015) — 511 citations
• ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels (2019) — 1,086 citations
• InceptionTime: Finding AlexNet for time series classification (2019) — 1,670 citations
• Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018) — 585 citations
• Analyzing concept drift and shift from sample data (2018) — 105 citations
• A global averaging method for dynamic time warping, with applications to clustering (2011) — 1,132 citations