School of Informatics AUTh
Nalmpantis, C., Vrysis, L., Vlachava, D., Papageorgiou L. & Vrakas D., "Noise invariant feature pooling for the internet of audio things". Multimed Tools Appl 81, 32057–32072 (2022). https://doi.org/10.1007/s11042-022-12931-y
Virtsionis Gkalinikis, Nikolaos, Christoforos Nalmpantis, and Dimitris Vrakas. 2022. "Torch-NILM: An Effective Deep Learning Toolkit for Non-Intrusive Load Monitoring in Pytorch" Energies 15, no. 7: 2647. https://doi.org/10.3390/en15072647
Nalmpantis, Christoforos, Nikolaos Virtsionis Gkalinikis, and Dimitris Vrakas. 2022. "Neural Fourier Energy Disaggregation" Sensors 22, no. 2: 473. https://doi.org/10.3390/s22020473
Virtsionis-Gkalinikis, N., Nalmpantis, C. & Vrakas, D. SAED: self-attentive energy disaggregation. Mach Learn (2021). https://doi.org/10.1007/s10994-021-06106-3
C. Nalmpantis, A. Lentzas and D. Vrakas, "A Theoretical Analysis of Pooling Operation Using Information Theory," 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 2019, pp. 1729-1733
A. Lentzas, C. Nalmpantis, D. Vrakas (2019) Hyperparameter Tuning using Quantum Genetic Algorithms. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 2019, pp. 1412-1416.
C. Nalmpantis, D. Vrakas, Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation, Artificial Intelligence Review, 2018