Florence d'Alché-Buc

Artificial Intelligence · 2001–2020

A researcher in the Science corpus: 6 papers; trusts 35 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #24,672 of 35,345 by trust here, #19,873 by citations
• Molecular Biology — 17 citations into it
• Statistical Learning Theory — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Signal Processing — 6 citations into it; #5,122 of 7,572 by trust here, #7,324 by citations
• Natural Language Processing — 5 citations into it

Whom Florence d'Alché-Buc trusts overall

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

1. Leo Breiman
2. L. Eon Bottou
3. Thorsten Joachims
4. Vladimir Naumovich Vapnik
5. Bernhard Schölkopf
6. Simon Justin Julier
7. Risi Kondor
8. Ioannis Tsochantaridis
9. Jason Aaron Edward Weston
10. Johan A. K. Suykens

Who trusts Florence d'Alché-Buc overall

1. Bryan Lim
2. Hachem Kadri
3. Céline Brouard
4. Peng Wang

Papers from Florence d'Alché-Buc's perspective

Ordered by how strongly the researchers Florence d'Alché-Buc 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) (2020) — 607 citations
• Gene networks inference using dynamic Bayesian networks (2003) — 501 citations
• Input Output Kernel Regression (2016)
• Semi-supervised Penalized Output Kernel Regression for Link Prediction (2011) — 74 citations
• Time series filtering, smoothing and learning using the kernel Kalman filter (2005) — 63 citations
• Incremental Support Vector Machine Learning: A Local Approach (2001) — 128 citations