| In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives A Amirsoleimani, F Alibart, V Yon, J Xu, MR Pazhouhandeh, S Ecoffey, ... Advanced Intelligent Systems 2 (11), 2000115, 2020 | 147 | 2020 |
| Miniaturizing neural networks for charge state autotuning in quantum dots S Czischek, V Yon, MA Genest, MA Roux, S Rochette, JC Lemyre, ... Machine Learning: Science and Technology 3 (1), 015001, 2021 | 15 | 2021 |
| Exploiting non-idealities of resistive switching memories for efficient machine learning V Yon, A Amirsoleimani, F Alibart, RG Melko, D Drouin, Y Beilliard Frontiers in Electronics 3, 825077, 2022 | 9 | 2022 |
| A cryogenic memristive neural decoder for fault-tolerant quantum error correction F Marcotte, PA Mouny, V Yon, GA Dagnew, B Kulchytskyy, S Rochette, ... arXiv preprint arXiv:2307.09463, 2023 | 1 | 2023 |
| Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars P Drolet, R Dawant, V Yon, PA Mouny, M Valdenaire, JA Zapata, P Gliech, ... arXiv preprint arXiv:2305.18495, 2023 | | 2023 |
| Adaptive filtering and classification for automated tuning of quantum dots into the single electron regime M Moras, MA Roux, L Njejimana, S Czischek, V Yon, MA Tétrault, ... APS March Meeting Abstracts 2023, M74. 010, 2023 | | 2023 |
| Robust quantum dot charge autotuning with Bayesian Neural Networks V Yon, S Czischek, Y Beilliard, M Pioro-Ladrière, G Louis, R Melko, ... Machine Learning in Natural Sciences: from Quantum Physics to Nanoscience …, 2022 | | 2022 |
| Quantum dot charge autotuning with Bayesian Neural Networks for spin qubit applications V Yon, S Czischek, Y Beilliard, M Pioro-Ladrière, G Louis, R Melko, ... Silicon Quantum Electronic Workshop, 2022 | | 2022 |
