no code implementations • 9 Apr 2024 • Fatima Ezzeddine, Mirna Saad, Omran Ayoub, Davide Andreoletti, Martin Gjoreski, Ihab Sbeity, Marc Langheinrich, Silvia Giordano
Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expected pattern of the majority of the data.
no code implementations • 8 Apr 2024 • Omran Ayoub, Davide Andreoletti, Aleksandra Knapińska, Róża Goścień, Piotr Lechowicz, Tiziano Leidi, Silvia Giordano, Cristina Rottondi, Krzysztof Walkowiak
In this work, we address this challenge for the problem of traffic forecasting and propose an approach that exploits adaptive learning algorithms, namely, liquid neural networks, which are capable of self-adaptation to abrupt changes in data patterns without requiring any retraining.
no code implementations • 4 Apr 2024 • Fatima Ezzeddine, Omran Ayoub, Silvia Giordano
To this end, we first propose a novel MEA methodology based on Knowledge Distillation (KD) to enhance the efficiency of extracting a substitute model of a target model exploiting CFs.
no code implementations • 30 Jan 2023 • Sandra Mitrović, Davide Andreoletti, Omran Ayoub
In this paper, we study whether a machine learning model can be effectively trained to accurately distinguish between original human and seemingly human (that is, ChatGPT-generated) text, especially when this text is short.
3 code implementations • 17 Oct 2022 • Fatima Ezzeddine, Luca Luceri, Omran Ayoub, Ihab Sbeity, Gianluca Nogara, Emilio Ferrara, Silvia Giordano
The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm.