297 citations · 434 across the 14 of their papers we have counts for
15 papers
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…
Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach
Rolandos Alexandros Potamias, Giorgos Bouritsas, Stefanos Zafeiriou
The recent advances in 3D sensing technology have made possible the capture of point clouds in significantly high resolution. However, increased detail usually comes at the expense…
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…
3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties
Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris +8
Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pi…
Learning to Generate Customized Dynamic 3D Facial Expressions
Rolandos Alexandros Potamias, Jiali Zheng, Stylianos Ploumpis +3
Recent advances in deep learning have significantly pushed the state-of-the-art in photorealistic video animation given a single image. In this paper, we extrapolate those advances…
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-…
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis +2
Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics. In this paper, we focus on 3D deformable shapes that share a common…
Multimodal Visual Concept Learning with Weakly Supervised Techniques
Giorgos Bouritsas, Petros Koutras, Athanasia Zlatintsi +1
Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to…