Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Robin Geneviève"'
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 73, Pp 238-256 (2023)
Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one – a behavior known as metastability. Simulating transition paths linking one metastable state to anoth
Externí odkaz:
https://doaj.org/article/ebb4f94197b34066b9288c6b295d022b
Overdamped Langevin dynamics are reversible stochastic differential equations which are commonly used to sample probability measures in high-dimensional spaces, such as the ones appearing in computational statistical physics and Bayesian inference. B
Externí odkaz:
http://arxiv.org/abs/2404.12087
The introduction of machine learning (ML) techniques to the field of survival analysis has increased the flexibility of modeling approaches, and ML based models have become state-of-the-art. These models optimize their own cost functions, and their p
Externí odkaz:
http://arxiv.org/abs/2302.12059
Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastable state to anothe
Externí odkaz:
http://arxiv.org/abs/2205.02818
Gaussian Graphical Models (GGMs) are widely used for exploratory data analysis in various fields such as genomics, ecology, psychometry. In a high-dimensional setting, when the number of variables exceeds the number of observations by several orders
Externí odkaz:
http://arxiv.org/abs/2202.05775
Publikováno v:
NeurIPS 2021 - 35th Conference on Neural Information Processing Systems, Dec 2021, Sydney, Australia
The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models. As in any other field of machine learning, applications of latent variable models to very large datasets make the use of advanced parallel a
Externí odkaz:
http://arxiv.org/abs/2111.02083
Autor:
Robin, Geneviève, Hasif, Cathia Le
Machine learning (ML) approaches are used more and more widely in biodiversity monitoring. In particular, an important application is the problem of predicting biodiversity indicators such as species abundance, species occurrence or species richness,
Externí odkaz:
http://arxiv.org/abs/2108.07480
Outliers arise in networks due to different reasons such as fraudulent behavior of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely obse
Externí odkaz:
http://arxiv.org/abs/1911.13122
Many applications of machine learning involve the analysis of large data frames-matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples-with missing values. Low-rank models, as studied by Udell et al. [30], are
Externí odkaz:
http://arxiv.org/abs/1812.08398
A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abundance data in ecology to recommender systems. In many cases, an MDF ex
Externí odkaz:
http://arxiv.org/abs/1806.09734