Michel Verleysen

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 14 papers; trusts 112 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #4,812 of 35,345 by trust here, #4,489 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #7,735 of 21,575 by trust here, #10,334 by citations
• Representation & Self-Supervised Learning — 52 citations into it; #2,711 of 3,145 by trust here, #2,910 by citations
• Statistical Learning Theory — 22 citations into it
• Statistics and Probability — 21 citations into it
• Signal Processing — 14 citations into it; #3,330 of 7,572 by trust here, #3,428 by citations
• Computational Theory and Mathematics — 12 citations into it; #1,441 of 12,182 by trust here, #1,157 by citations
• Evaluation & Benchmarks — 12 citations into it; #2,669 of 3,147 by trust here, #3,134 by citations

Whom Michel Verleysen trusts overall

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

1. Geoffrey E. Hinton
2. Mark A. Kramer
3. Josh Tenenbaum
4. Sam T. Roweis
5. Bernhard Schölkopf
6. Mikhail Belkin
7. L. V. D. van der Maaten
8. Carla E. Brodley
9. Jerome H. Friedman
10. Yann LeCun

Who trusts Michel Verleysen overall

1. John Aldo Lee
2. Zhiqiang Guo
3. Jacob Goldberger
4. Himanshu Kumar
5. Omid Kashefi
6. Jessa Bekker
7. Leo Liberti
8. Dmitry Kobak
9. Matthew O. Williams
10. Kuang-Huei Lee

Papers from Michel Verleysen's perspective

Ordered by how strongly the researchers Michel Verleysen 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Classification in the Presence of Label Noise: A Survey (2014) — 1,820 citations
• Quality assessment of dimensionality reduction: Rank-based criteria (2009) — 334 citations
• The Curse of Dimensionality in Data Mining and Time Series Prediction (2005) — 783 citations
• Perplexity-free t-SNE and twice Student tt-SNE (2018) — 20 citations
• Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure (2015) — 98 citations
• Unique in the Crowd: The privacy bounds of human mobility (2013) — 1,701 citations
• Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation (2013) — 102 citations
• Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs (2011) — 21 citations
• Shift-invariant similarities circumvent distance concentration in stochastic neighbor embedding and variants (2011) — 40 citations
• Clustering Patterns of Urban Built-up Areas with Curves of Fractal Scaling Behaviour (2010) — 63 citations
• Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar] (2009) — 30 citations
• Forecasting the CATS benchmark with the Double Vector Quantization method (2007) — 12 citations
• Nonlinear Dimensionality Reduction (2007) — 409 citations
• The Concentration of Fractional Distances (2007) — 324 citations