Publications
Publications
|
|
Unbiased Approximate Vector-Jacobian Products for Efficient Backpropagation2026 |
|
|
Vectorizing string entries for data processing on tables: when are larger language models better?2023 |
|
|
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning2021 |
|
|
Interferometric Graph Transform for Community Labeling2021 |
|
|
Nonlinear Acceleration of Deep Neural Networks2018 |
|
|
PETRA: Parallel End-to-end Training with Reversible ArchitecturesThe Thirteenth International Conference on Learning Representations, Apr 2025, Singapour, Singapore |
|
|
DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic predictionInternational Conference on Machine Learning, Jul 2025, Vancouver, Canada |
|
|
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingThe Thirty-ninth Annual Conference on Neural Information Processing Systems, Dec 2025, San diego (Californie), United States |
|
|
$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep LearningThirty-seventh Conference on Neural Information Processing Systems, Dec 2023, New Orleans, United States |
|
|
Can Forward Gradient Match Backpropagation?Fortieth International Conference on Machine Learning, Jul 2023, Honolulu (Hawaii), USA, United States |
|
|
Preventing Dimensional Collapse in Contrastive Local Learning with SubsamplingICML 2023 Workshop on Localized Learning (LLW), Jul 2023, Honolulu (Hawaii), USA, United States |
|
|
DADAO: Decoupled Accelerated Decentralized Asynchronous OptimizationInternational Conference on Machine Learning, Jul 2023, Honolulu, United States |
|
|
Why do tree-based models still outperform deep learning on typical tabular data?36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, Nov 2022, New Orleans, United States |
|
|
On Non-Linear operators for Geometric Deep LearningConference on Neural Information Processing Systems (Neurips), Dec 2022, New Orleans, United States. ⟨10.48550/arXiv.2207.03485⟩ |
|
|
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels MethodsInternational Conference on Learning Representation (ICLR 2021), 2021, Vienna (online), Austria |
|
|
Low-Rank Projections of GCNs LaplacianICLR 2021 Workshop GTRL, May 2021, Online, France |
|
|
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia AssessmentMachine Learning in Medical Imaging @MICCAI, Sep 2021, Strasbourg, France. pp.317-326, ⟨10.1007/978-3-030-87589-3_33⟩ |
|
|
Decoupled Greedy Learning of CNNsInternational Conference on Machine Learning, Jul 2020, Vienna (virtual), Austria. pp.5368-5377 |
|
|
Interferometric Graph Transform: a Deep Unsupervised Graph Representation37th International Conference on Machine Learning (ICML 2020), Jul 2020, Online, Austria |
|
|
On Lazy Training in Differentiable ProgrammingNeurIPS 2019 - 33rd Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada. pp.2937-2947 |
|
|
Greedy Layerwise Learning Can Scale to ImageNetICML 2019 - 36th International Conference on Machine Learning, Jun 2019, Long Beach, CA, United States |
|
|
i-RevNet: Deep Invertible NetworksICLR 2018 - International Conference on Learning Representations, Apr 2018, Vancouver, Canada |
|
|
Nonlinear Acceleration of CNNsICLR Workshop track, Apr 2018, Vancouver, Canada |
|
|
Compressing the Input for CNNs with the First-Order Scattering TransformECCV 2018 - European Conference on Computer Vision, Sep 2018, Munich, Germany |
|
|
Scaling the Scattering Transform: Deep Hybrid NetworksInternational Conference on Computer Vision (ICCV), Oct 2017, Venice, Italy |
|
|
Decentralized Asynchronous Optimization with DADAO allows Decoupling and AccelerationJournal of Machine Learning Research, 2025, 26 (207), pp.1-48 |
|
|
Guiding The Last Layer in Federated Learning with Pre-Trained ModelsNeurips, In press |
|
|
Gradient Masked Averaging for Federated LearningTransactions on Machine Learning Research Journal, 2022 |
|
|
Kymatio: Scattering Transforms in PythonJournal of Machine Learning Research, 2020, 21 (60), pp.1-6 |
|
|
Scattering Networks for Hybrid Representation LearningIEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, pp.11. ⟨10.1109/TPAMI.2018.2855738⟩ |
|
|
An Analysis of the SURF MethodImage Processing On Line, 2015, 5, pp.176 - 218. ⟨10.5201/ipol.2015.69⟩ |
|
|
Contributions to Local, Asynchronous and Decentralized Learning, and to Geometric Deep LearningArtificial Intelligence [cs.AI]. Sorbonne Université, 2023 |
|
|
Analyzing and introducing structures in deep convolutional neural networksComputer Vision and Pattern Recognition [cs.CV]. Université Paris sciences et lettres, 2017. English. ⟨NNT : 2017PSLEE060⟩ |