Project-Team:MODAL

Project-Team Modal

Members

Overall Objectives

Research Program

Generative model design
Data visualization

Application Domains

Multiple domains applications
Genomics

Highlights of the Year

New Software and Platforms

BlockCluster
Clustericat
CoModes
CorReg
FunFEM
FunHDDC
Galaxy - MPAgenomics
HDPenReg
MPAGenomics
MetaMA
MetaRNASeq
MixAll
MixCluster
Mixmod
MixtComp
RankCluster
STK++
clere
rtkore

New Results

An oracle inequality for Quasi-Bayesian Non-Negative Matrix Factorisation
PAC-Bayesian Online Clustering
Simpler PAC-Bayesian Bounds for Hostile Data
Clustering categorical functional data: Application to medical discharge letters
Simultaneous dimension reduction and multi-objective clustering
Spatial Prediction of solar energy
Multiple change-point detection
Differential gene expression analysis
New concentration inequalities for the leave-p-out CV estimator
A new notion of stability for learning algorithms
Model for conditionally correlated categorical data
Mixture model for mixed kind of data
Degeneracy in multivariate Gaussian mixtures (complete data case)
Degeneracy in multivariate Gaussian mixtures (missing data case)
Data units selection in statistics
Label switching in Bayesian mixture model estimation
Trade-off computation time and accuracy
Projection under pairwise control
Matching of descriptors evolving over time
Real-time audio sources classification
Model-Based Co-clustering for Ordinal Data
Computational and statistical trade-offs in change-point detection
MixtComp software for full mixed data
MASSICCC platform for SaaS software availability
CoModes package for correlated categorical variables
MixCluster package for correlated mixed variables

Bilateral Contracts and Grants with Industry

Arcelor-Mittal
Banque Accord
Vallourec
Cylande
NFID

Partnerships and Cooperations

Regional Initiatives
National Initiatives
European Initiatives
International Initiatives
International Research Visitors

Dissemination

Promoting Scientific Activities
Teaching - Supervision - Juries
Popularization

Bibliography

Major publications
Publications of the year

Inria | Raweb 2016 | Presentation of the Project-Team MODAL


	PDF	e-Pub

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Section: Bilateral Contracts and Grants with Industry

Arcelor-Mittal

Participant : Christophe Biernacki.

Arcelor-Mittal is a leader company in steel industry. This 11 months contract aims at optimizing predictive maintenance from mixed data (continuous, categorical, functional) provided by multiple sensors disseminated in steel production lines.

It is a joint work with Martin Bue and Vincent Kubicki (InriaTech engineers).

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