Aymeric Dieuleveut
Artificial Intelligence · 2014–2025
A researcher in the Science corpus: 9 papers; trusts 438 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 224 citations into it; #5,022 of 35,345 by trust here, #7,858 by citations
- Optimization & Training — 150 citations into it; #682 of 2,601 by trust here, #1,280 by citations
- Computer Vision and Pattern Recognition — 71 citations into it
- Computational Mechanics — 67 citations into it; #1,534 of 10,001 by trust here, #1,985 by citations
- Natural Language Processing — 54 citations into it
- Statistics and Probability — 44 citations into it; #2,994 of 12,932 by trust here, #3,801 by citations
- Representation & Self-Supervised Learning — 38 citations into it; #1,873 of 3,145 by trust here, #2,454 by citations
- Management Science and Operations Research — 30 citations into it
Whom Aymeric Dieuleveut trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Francis R. Bach
- Tong Zhang
- Ohad Shamir
- Geoffrey E. Hinton
- Eric Moulines
- Yoshua Bengio
- Alex Krizhevsky
- Diederik P. Kingma
- L. Eon Bottou
- Yurii E. Nesterov
Who trusts Aymeric Dieuleveut overall
- Kumar Kshitij Patel
- Francis R. Bach
- Prateek Jain
- Alessandro Rudi
- Constantin Philippenko
- Sham M. Kakade
- Dmitry Babichev
- Sai Praneeth Karimireddy
- Rahul Kidambi
- Sebastian U. Stich
Papers from Aymeric Dieuleveut's perspective
Ordered by how strongly the researchers Aymeric Dieuleveut trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Non-parametric Stochastic Approximation with Large Step sizes (2014) — 194 citations
- Bridging the gap between constant step size stochastic gradient descent and Markov chains (2017) — 191 citations
- A benchmark of expert-level academic questions to assess AI capabilities (2025) — 349 citations
- Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression (2016) — 244 citations
- Communication trade-offs for synchronized distributed SGD with large step size (2019) — 27 citations
- Artemis: tight convergence guarantees for bidirectional compression in Federated Learning (2020) — 22 citations
- Unsupervised Scalable Representation Learning for Multivariate Time Series (2019) — 638 citations
- Communication trade-offs for Local-SGD with large step size (2019) — 44 citations
- Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations (2018) — 27 citations