27 results on '"Stéphane Marchand"'
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2. FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking.
3. Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction.
4. Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph.
5. HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph Attention.
6. Supervised Auto-Encoding Twin-Bottleneck Hashing.
7. H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression.
8. Cold Start Active Learning Strategies in the Context of Imbalanced Classification.
9. Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features.
10. Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs.
11. Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders.
12. Computing flood probabilities using Twitter: application to the Houston urban area during Harvey.
13. Extracting localized information from a Twitter corpus for flood prevention.
14. Learning by stochastic serializations.
15. The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets.
16. Adjacency and Tensor Representation in General Hypergraphs.Part 2: Multisets, Hb-graphs and Related e-adjacency Tensors.
17. Exchange-Based Diffusion in Hb-Graphs: Highlighting Complex Relationships.
18. Large-scale Nonlinear Variable Selection via Kernel Random Features.
19. On Adjacency and e-Adjacency in General Hypergraphs: Towards a New e-Adjacency Tensor.
20. Structured nonlinear variable selection.
21. Hypergraph Modeling and Visualisation of Complex Co-occurence Networks.
22. On Hölder projective divergences.
23. Networks of Collaborations: Hypergraph Modeling and Visualisation.
24. Forecasting and Granger Modelling with Non-linear Dynamical Dependencies.
25. Adjacency and Tensor Representation in General Hypergraphs Part 1: e-adjacency Tensor Uniformisation Using Homogeneous Polynomials
26. Learning vector autoregressive models with focalised Granger-causality graphs.
27. Two-Stage Metric Learning.
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