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. Although this approach has been successful, other approaches that do not discard part of the samples can also be considered. For example, by using tensor versions of principal component analysis [DEL00
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. Latouche and D. Liang, Clustering by Deep Latent Position Model with Graph Convolutional Network, Advances in Data Analysis and Classification, in press, 2024 - C. Bouveyron, M. Corneli and G. Marchello, A
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), pp. 11-31, 2017 - C. Bouveyron, M. Corneli, P. Latouche and D. Liang, Clustering by Deep Latent Position Model with Graph Convolutional Network, Advances in Data Analysis and Classification, in press
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such as principal component analysis (PCA) [2] as well as new types of attacks like link stealing attacks [3] whereby the protected information is not just a dataset but has more complex structure (such as
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-based learning or reinforcement learning, gradients are either unavailable or unreliable. In such contexts, zeroth-order (blackbox) optimization becomes essential. Blackbox methods, such as finite
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for millions of elements Traditional methods based on Delaunay triangulation (3D mesh generation, 3D alpha wrapping) typically strive to achieve high geometric accuracy and high-quality elements. While
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Josse. Estimation and imputation in probabilistic principal component analysis with missing not at random data. NeurIPS, 2020 Daniel J Stekhoven and Peter Buhlmann. Missforest—non-parametric missing value
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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for the analysis of the sequence data, to describe the genetic diversity between French regions for different types of variants (SNV, indels, SV, STR, mictochondrial…). He/she will work in collaboration with our
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analysis of biomedical data and bioscientific programming for a project on the study of neurological disorders. The candidate should have experience in the analysis of large-scale biomedical data (e.g