Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks. - Odysseas Krystalakos; Christoforos Nalmpantis; Dimitris Vrakas (SETN, 2018)

Energy profile representation in vector space. - Christoforos Nalmpantis; Odysseas Krystalakos; Dimitris Vrakas (SETN, 2018)

Data Expedition into the Swiss Twitter Corpus. - Ralf Grubenmann; William Fallouh; Christoforos Nalmpantis; Mark Cieliebak (SwissText, 2018)

Imaging Time-Series for NILM. - Lamprini Kyrkou; Christoforos Nalmpantis; Dimitris Vrakas (EANN, 2019)

Signal2Vec: Time Series Embedding Representation. - Christoforos Nalmpantis; Dimitris Vrakas (EANN, 2019)

A Benchmark Framework to Evaluate Energy Disaggregation Solutions. - Nikolaos Symeonidis; Christoforos Nalmpantis; Dimitris Vrakas (EANN, 2019)

Hyperparameter Tuning using Quantum Genetic Algorithms. - Athanasios Lentzas; Christoforos Nalmpantis; Dimitris Vrakas (ICTAI, 2019)

A Theoretical Analysis of Pooling Operation Using Information Theory. - Christoforos Nalmpantis; Athanasios Lentzas; Dimitris Vrakas (ICTAI, 2019)

Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. - Christoforos Nalmpantis; Dimitris Vrakas (Artif. Intell. Rev., 2019)

Attention in Recurrent Neural Networks for Energy Disaggregation. - Nikolaos Virtsionis Gkalinikis; Christoforos Nalmpantis; Dimitris Vrakas (DS, 2020)

On time series representations for multi-label NILM. - Christoforos Nalmpantis; Dimitris Vrakas (Neural Comput. Appl., 2020)

Entropy Based Feature Pooling in Speech Command Classification. - Christoforos Nalmpantis; Lazaros Vrysis; Danai Vlachava; Lefteris Papageorgiou; Dimitris Vrakas (SAI (3), 2021)

Noise invariant feature pooling for the internet of audio things. - Christoforos Nalmpantis; Lazaros Vrysis; Danai Vlachava; Lefteris Papageorgiou; Dimitris Vrakas (Multim. Tools Appl., 2022)

Neural Fourier Energy Disaggregation. - Christoforos Nalmpantis; Nikolaos Virtsionis Gkalinikis; Dimitris Vrakas (Sensors, 2022)

Machine learning approaches for time series problems - Christoforos Nalmpantis (2022)

PEER: A Collaborative Language Model. - Timo Schick; Jane A. Yu; Zhengbao Jiang; Fabio Petroni; Patrick Lewis 001; Gautier Izacard; Qingfei You; Christoforos Nalmpantis; Edouard Grave; Sebastian Riedel 001 (ICLR, 2023)

Augmented Language Models: a Survey. - Grégoire Mialon; Roberto Dessì; Maria Lomeli; Christoforos Nalmpantis; Ramakanth Pasunuru; Roberta Raileanu; Baptiste Rozière; Timo Schick; Jane Dwivedi-Yu; Asli Celikyilmaz; Edouard Grave; Yann LeCun; et al. (Trans. Mach. Learn. Res., 2023)

Variational Regression for Multi-Target Energy Disaggregation. - Nikolaos Virtsionis Gkalinikis; Christoforos Nalmpantis; Dimitris Vrakas (Sensors, 2023)

SAED: self-attentive energy disaggregation. - Nikolaos Virtsionis Gkalinikis; Christoforos Nalmpantis; Dimitris Vrakas (Mach. Learn., 2023)

Neurons in Large Language Models: Dead, N-gram, Positional. - Elena Voita; Javier Ferrando; Christoforos Nalmpantis (ACL (Findings), 2024)

Understanding the Effects of RLHF on LLM Generalisation and Diversity. - Robert Kirk; Ishita Mediratta; Christoforos Nalmpantis; Jelena Luketina; Eric Hambro; Edward Grefenstette; Roberta Raileanu (ICLR, 2024)

GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements. - Alexander Havrilla; Sharath Chandra Raparthy; Christoforos Nalmpantis; Jane Dwivedi-Yu; Maksym Zhuravinskyi; Eric Hambro; Roberta Raileanu (ICML, 2024)

GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements. - Alex Havrilla; Sharath Chandra Raparthy; Christoforos Nalmpantis; Jane Dwivedi-Yu; Maksym Zhuravinskyi; Eric Hambro; Roberta Railneau (CoRR, 2024)

Teaching Large Language Models to Reason with Reinforcement Learning. - Alex Havrilla; Yuqing Du; Sharath Chandra Raparthy; Christoforos Nalmpantis; Jane Dwivedi-Yu; Maksym Zhuravinskyi; Eric Hambro; Sainbayar Sukhbaatar; Roberta Raileanu (CoRR, 2024)