| Augmented language models: a survey G Mialon, R Dessì, M Lomeli, C Nalmpantis, R Pasunuru, R Raileanu, ... arXiv preprint arXiv:2302.07842, 2023 | 513 | 2023 |
| Sliding window approach for online energy disaggregation using artificial neural networks O Krystalakos, C Nalmpantis, D Vrakas Proceedings of the 10th Hellenic Conference on Artificial Intelligence, 1-6, 2018 | 183 | 2018 |
| Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation C Nalmpantis, D Vrakas Artificial Intelligence Review 52, 217-243, 2019 | 164 | 2019 |
| Understanding the effects of rlhf on llm generalisation and diversity R Kirk, I Mediratta, C Nalmpantis, J Luketina, E Hambro, E Grefenstette, ... arXiv preprint arXiv:2310.06452, 2023 | 80 | 2023 |
| On time series representations for multi-label NILM C Nalmpantis, D Vrakas Neural Computing and Applications 32, 17275-17290, 2020 | 48 | 2020 |
| Signal2vec: Time series embedding representation C Nalmpantis, D Vrakas International conference on engineering applications of neural networks, 80-90, 2019 | 44 | 2019 |
| Peer: A collaborative language model T Schick, J Dwivedi-Yu, Z Jiang, F Petroni, P Lewis, G Izacard, Q You, ... arXiv preprint arXiv:2208.11663, 2022 | 42 | 2022 |
| Teaching large language models to reason with reinforcement learning A Havrilla, Y Du, SC Raparthy, C Nalmpantis, J Dwivedi-Yu, ... arXiv preprint arXiv:2403.04642, 2024 | 41 | 2024 |
| Neurons in large language models: Dead, n-gram, positional E Voita, J Ferrando, C Nalmpantis arXiv preprint arXiv:2309.04827, 2023 | 35 | 2023 |
| Imaging time-series for NILM L Kyrkou, C Nalmpantis, D Vrakas International Conference on Engineering Applications of Neural Networks, 188-196, 2019 | 25 | 2019 |
| SAED: Self-attentive energy disaggregation N Virtsionis-Gkalinikis, C Nalmpantis, D Vrakas Machine Learning, 1-20, 2021 | 23 | 2021 |
| Neural Fourier energy disaggregation C Nalmpantis, N Virtsionis Gkalinikis, D Vrakas Sensors 22 (2), 473, 2022 | 22 | 2022 |
| Torch-nilm: An effective deep learning toolkit for non-intrusive load monitoring in pytorch N Virtsionis Gkalinikis, C Nalmpantis, D Vrakas Energies 15 (7), 2647, 2022 | 18 | 2022 |
| Attention in recurrent neural networks for energy disaggregation N Virtsionis Gkalinikis, C Nalmpantis, D Vrakas Discovery Science: 23rd International Conference, DS 2020, Thessaloniki …, 2020 | 12 | 2020 |
| Variational regression for multi-target energy disaggregation N Virtsionis Gkalinikis, C Nalmpantis, D Vrakas Sensors 23 (4), 2051, 2023 | 11 | 2023 |
| Hyperparameter tuning using quantum genetic algorithms A Lentzas, C Nalmpantis, D Vrakas 2019 IEEE 31st International Conference on Tools with Artificial …, 2019 | 11 | 2019 |
| Augmented Language Models: a Survey,(2023) G Mialon, R Dessì, M Lomeli, C Nalmpantis, R Pasunuru, R Raileanu, ... URL: https://arxiv. org/abs/2302.07842. doi 10, 48550, 0 | 11 | |
| A benchmark framework to evaluate energy disaggregation solutions N Symeonidis, C Nalmpantis, D Vrakas International Conference on Engineering Applications of Neural Networks, 19-30, 2019 | 10 | 2019 |
| Augmented language models: A survey. ArXiv 2023 G Mialon, R Dessi, M Lomeli, C Nalmpantis, R Pasunuru, R Raileanu, ... arXiv preprint arXiv:2302.07842, 0 | 7 | |
| Energy profile representation in vector space C Nalmpantis, O Krystalakos, D Vrakas Proceedings of the 10th Hellenic Conference on Artificial Intelligence, 1-5, 2018 | 6 | 2018 |
Dimitris VrakasSchool of Informatics, Aristotle University of Thessaloniki在 csd.auth.gr 的电子邮件经过验证