Your Moves, Your Device: Establishing Behavior Profiles Using Tensors. - Eric Falk; Jérémy Charlier; Radu State (ADMA, 2017)

Profiling Smart Contracts Interactions Tensor Decomposition and Graph Mining. - Jérémy Charlier; Sofiane Lagraa; Radu State; Jérôme François (MIDAS@PKDD/ECML, 2017)

Non-negative Paratuck2 Tensor Decomposition Combined to LSTM Network for Smart Contracts Profiling. - Jérémy Charlier; Radu State; Jean Hilger (BigComp, 2018)

User-Device Authentication in Mobile Banking Using APHEN for Paratuck2 Tensor Decomposition. - Jérémy Charlier; Eric Falk; Radu State; Jean Hilger (ICDM Workshops, 2018)

Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment. - Jérémy Charlier; Radu State; Jean Hilger (Canadian AI, 2019)

MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning. - Jérémy Charlier; Gaston Ormazabal; Radu State; Jean Hilger (MIDAS@PKDD, 2019)

PHom-GeM: Persistent Homology for Generative Models. - Jérémy Charlier; Radu State; Jean Hilger (SDS, 2019)

Modeling Smart Contracts Activities: A Tensor Based Approach. - Jérémy Charlier; Radu State; Jean Hilger (CoRR, 2019)

VecHGrad for solving accurately complex tensor decomposition. - Jérémy Charlier; Gaston Ormazabal; Radu State; Jean Hilger (CoRR, 2019)

MQLV: Modified Q-Learning for Vasicek Model. - Jérémy Charlier; Gaston Ormazabal; Radu State; Jean Hilger (CoRR, 2019)

PHom-WAE: Persitent Homology for Wasserstein Auto-Encoders. - Jérémy Charlier; François Petit; Gaston Ormazabal; Radu State; Jean Hilger (CoRR, 2019)

SynGAN: Towards Generating Synthetic Network Attacks using GANs. - Jérémy Charlier; Aman Singh; Gaston Ormazabal; Radu State; Henning Schulzrinne (CoRR, 2019)

From Persistent Homology to Reinforcement Learning with Applications for Retail Banking. - Jérémy Charlier (CoRR, 2019)

VecHGrad for Solving Accurately Tensor Decomposition. - Jérémy Charlier; Vladimir Makarenkov (Canadian AI, 2020)

XtracTree for Regulator Validation of Bagging Methods Used in Retail Banking. - Jérémy Charlier; Vladimir Makarenkov (CoRR, 2020)

Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing. - Jérémy Charlier; Robert Nadon; Vladimir Makarenkov (Bioinform., 2021)

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review. - Zeinab Sherkatghanad; Moloud Abdar; Jérémy Charlier; Vladimir Makarenkov (Briefings Bioinform., 2023)