| Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing J Charlier, R Nadon, V Makarenkov Bioinformatics 37 (16), 2299-2307, 2021 | 28 | 2021 |
| SynGAN: Towards generating synthetic network attacks using GANs J Charlier, A Singh, G Ormazabal, R State, H Schulzrinne arXiv preprint arXiv:1908.09899, 2019 | 26 | 2019 |
| R. State, and H. Schulzrinne,“Syngan: Towards generating synthetic network attacks using gans,” J Charlier, A Singh, G Ormazabal arXiv preprint arXiv:1908.09899, 2019 | 15 | 2019 |
| Using traditional machine learning and deep learning methods for on-and off-target prediction in CRISPR/Cas9: a review Z Sherkatghanad, M Abdar, J Charlier, V Makarenkov Briefings in Bioinformatics 24 (3), bbad131, 2023 | 12 | 2023 |
| Profiling smart contracts interactions with tensor decomposition and graph mining J Charlier, S Lagraa, J Francois European Conference on Machine Learning and Principles and Practice of …, 2017 | 10 | 2017 |
| State, R.; Schulzrinne, H. SynGAN: Towards generating synthetic network attacks using GANs. arXiv 2019 J Charlier, A Singh, G Ormazabal arXiv preprint arXiv:1908.09899, 0 | 6 | |
| Radu State, and Henning Schulzrinne. Syngan: Towards generating synthetic network attacks using gans J Charlier, A Singh, G Ormazabal arXiv preprint arXiv:1908.09899, 2019 | 5 | 2019 |
| Non-negative paratuck2 tensor decomposition combined to LSTM network for smart contracts profiling J Charlier, R State, J Hilger 2018 IEEE International Conference on Big Data and Smart Computing (BigComp …, 2018 | 5 | 2018 |
| PHom-GeM: Persistent homology for generative models J Charlier, R State, J Hilger 2019 6th Swiss Conference on Data Science (SDS), 87-92, 2019 | 3 | 2019 |
| User-device authentication in mobile banking using APHEN for PARATUCK2 tensor decomposition J Charlier, E Falk, R State, J Hilger 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 886-894, 2018 | 3 | 2018 |
| MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning J Charlier, G Ormazabal, R State, J Hilger Mining Data for Financial Applications: 4th ECML PKDD Workshop, MIDAS 2019 …, 2020 | 2 | 2020 |
| VecHGrad for solving accurately complex tensor decomposition J Charlier, V Makarenkov arXiv preprint arXiv:1905.12413, 2019 | 2 | 2019 |
| Modeling smart contracts activities: A tensor based approach J Charlier, R Statem, J Hilger arXiv preprint arXiv:1905.09868, 2019 | 2 | 2019 |
| Your Moves, Your Device: Establishing Behavior Profiles Using Tensors E Falk, J Charlier, R State Advanced Data Mining and Applications: 13th International Conference, ADMA …, 2017 | 2 | 2017 |
| XtracTree for regulator validation of bagging methods used in retail banking J Charlier, V Makarenkov CoRR, 2020 | 1 | 2020 |
| Novel Encoding of sgRNA-DNA Sequences for Accurate Deep Learning Off-Target Predictions J Charlier, V Makarenkov | | 2020 |
| XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking J Charlier, V Makarenkov arXiv preprint arXiv:2004.02326, 2020 | | 2020 |
| VecHGrad for Solving Accurately Tensor Decomposition J Charlier, V Makarenkov Advances in Artificial Intelligence: 33rd Canadian Conference on Artificial …, 2020 | | 2020 |
| From Persistent Homology to Reinforcement Learning with Applications for Retail Banking J Charlier arXiv preprint arXiv:1911.11573, 2019 | | 2019 |
| Visualization of AE's Training on Credit Card Transactions with Persistent Homology J Charlier, F Petit, G Ormazabal, R State, J Hilger arXiv preprint arXiv:1905.13020, 2019 | | 2019 |
Jérôme FRANCOISInria Nancy Grand EstVerified email at inria.fr
