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CHARLIER Jérémy Henri J.

Main Referenced Co-authors
STATE, Radu  (11)
HILGER, Jean  (5)
FALK, Eric  (2)
Hilger, Jean (2)
LAGRAA, Sofiane  (2)
Main Referenced Keywords
Barcodes (2); Neural Networks (2); Q-learning (2); Tensor (2); Algebraic Topology (1);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN) (11)
Main Referenced Disciplines
Computer science (12)

Publications (total 12)

The most downloaded
331 downloads
Charlier, J. H. J., & State, R. (April 2018). Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network for Smart Contracts Profiling. International Journal of Computer & Software Engineering, 3 (1). doi:10.15344/2456-4451/2018/132 https://hdl.handle.net/10993/35508

The most cited

5 citations (Scopus®)

Charlier, J. H. J., State, R., & Hilger, J. (2018). Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling. In J. Charlier, R. State, ... J. Hilger, 2018 IEEE International Conference on Big Data and Smart Computing Proceedings (pp. 74-81). IEEE Computer Society Conference Publishing Services (CPS). doi:10.1109/BigComp.2018.00020 https://hdl.handle.net/10993/34803

Charlier, J. H. J. (2019). From Persistent Homology to Reinforcement Learning with Applications for Retail Banking [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/40891

Charlier, J. H. J., Ormazabal, G., State, R., & Hilger, J. (2019). MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning. Proceedings of the Fourth Workshop on MIning DAta for financial applicationS (MIDAS 2019) co-located with the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). doi:10.1007/978-3-030-37720-5_1
Peer reviewed

Charlier, J. H. J., Petit, F., Ormazabal, G., State, R., & Hilger, J. (2019). Visualization of AE's Training on Credit Card Transactions with Persistent Homology. Proceedings of the International Workshop on Applications of Topological Data Analysis In conjunction with ECML PKDD 2019.
Peer reviewed

Charlier, J. H. J., State, R., & Hilger, J. (2019). PHom-GeM: Persistent Homology for Generative Models. The 6th Swiss Conference on Data Science. doi:10.1109/SDS.2019.000-1
Peer reviewed

Charlier, J. H. J., State, R., & Hilger, J. (2019). Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment. 32nd Canadian Conference on Artificial Intelligence Proceedings. doi:10.1007/978-3-030-18305-9_59
Peer reviewed

Charlier, J. H. J., Falk, E., State, R., & Hilger, J. (2018). User-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition. Proceedings of 2018 IEEE International Conference on Data Mining Workshops (ICDMW). doi:10.1109/ICDMW.2018.00130
Peer reviewed

Lagraa, S., Charlier, J. H. J., & State, R. (20 August 2018). Knowledge Discovery Approach from Blockchain, Crypto-currencies, and Financial Stock Exchanges [Poster presentation]. 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data mining conference (KDD 2018).

Charlier, J. H. J., & State, R. (April 2018). Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network for Smart Contracts Profiling. International Journal of Computer & Software Engineering, 3 (1). doi:10.15344/2456-4451/2018/132

Charlier, J. H. J., State, R., & Hilger, J. (2018). Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling. In J. Charlier, R. State, ... J. Hilger, 2018 IEEE International Conference on Big Data and Smart Computing Proceedings (pp. 74-81). IEEE Computer Society Conference Publishing Services (CPS). doi:10.1109/BigComp.2018.00020
Peer reviewed

Falk, E., Charlier, J. H. J., & State, R. (2017). Your Moves, Your Device: Establishing Behavior Profiles Using Tensors. In Advanced Data Mining and Applications - 13th International Conference, ADMA 2017 (pp. 460-474).
Peer reviewed

Charlier, J. H. J., State, R., & Hilger, J. (2017). Modeling Smart Contracts Activities: A Tensor based Approach. In J. Charlier, R. State, ... J. Hilger, Proceedings of 2017 Future Technologies Conference (FTC), 29-30 November 2017, Vancouver, Canada (pp. 49-55). IEEE.
Peer reviewed

Charlier, J. H. J., Lagraa, S., State, R., & Francois, J. (2017). Profiling Smart Contracts Interactions Tensor Decomposition and Graph Mining. In Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18, 2017 (pp. 31-42).
Peer reviewed

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