297 citations · 434 across the 14 of their papers we have counts for
10 papers · 1 filter
Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance
Ioannis Kalogeropoulos, Giorgos Bouritsas, Yannis Panagakis
As machine learning models are increasingly deployed in high-stakes settings, e.g. as decision support systems in various societal sectors or in critical infrastructure, designers…
Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search
Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros +1
Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gende…
A Principled Framework for Multi-View Contrastive Learning
Panagiotis Koromilas, Efthymios Georgiou, Giorgos Bouritsas +3
Contrastive Learning (CL), a leading paradigm in Self-Supervised Learning (SSL), typically relies on pairs of data views generated through augmentation. While multiple augmentation…
Scale Equivariant Graph Metanetworks
Ioannis Kalogeropoulos, Giorgos Bouritsas, Yannis Panagakis
This paper pertains to an emerging machine learning paradigm: learning higher-order functions, i.e. functions whose inputs are functions themselves, $\textit{particularly when thes…
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses
Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos +2
What do different contrastive learning (CL) losses actually optimize for? Although multiple CL methods have demonstrated remarkable representation learning capabilities, the differ…
Partition and Code: learning how to compress graphs
Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias +1
Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainabl…
Deep Polynomial Neural Networks
Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas +3
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. T…
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou +1
While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the stru…
nets: Deep Polynomial Neural Networks
Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas +3
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. Th…
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks
Giorgos Bouritsas, Stelios Daveas, Antonios Danelakis +2
Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-…