63 citations · 237 across the 33 of their papers we have counts for
39 papers
Validity-Preserving Hierarchical RL for Joint Routing and Switch Placement in EDA
Dorian Gailhard, Ugo Lecerf, Enzo Tartaglione +2
Routing and switch placement are fundamental combinatorial optimization problems in chip design, requiring the joint optimization of routing topology and physical placement under s…
Graph-Spectral Flow Matching for Multivariate Time Series Anomaly Detection
Zepeng Zhang, Jhony H. Giraldo, Wenbin Wang +1
Multivariate time series anomaly detection typically relies on evaluating discrepancies between observations and outputs produced by models trained on normal data. An alternative p…
Spatiotemporal Imputation with Graph-Informed Flow Matching
Zepeng Zhang, Aref Einizade, Jhony H. Giraldo +1
Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning appr…
Scaling Higher-Order Graph Learning with Maximal Clique Complexes
Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros +1
Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poo…
Generalization Bounds for Spectral GNNs via Fourier Domain Analysis
Vahan A. Martirosyan, Daniele Malitesta, Hugues Talbot +2
Spectral graph neural networks learn graph filters, but their behavior with increasing depth and polynomial order is not well understood. We analyze these models in the graph Fouri…
WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift
Julian D. Santamaria, Claudia Isaza, Jhony H. Giraldo
Wildlife monitoring is crucial for studying biodiversity loss and climate change. Camera trap images provide a non-intrusive method for analyzing animal populations and identifying…
Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention
Lucas Li, Jean-Baptiste Puel, Florence Carton +2
Perovskite solar cells are promising candidates for next-generation photovoltaics. However, their performance as multi-scale devices is determined by complex interactions between t…
Second-Order Tensorial Partial Differential Equations on Graphs
Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
Processing data on multiple interacting graphs is crucial for many applications, but existing approaches rely mostly on discrete filtering or first-order continuous models, dampeni…
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning
Shifeng Xie, Aref Einizade, Jhony H. Giraldo
Graph Representation Learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-Supervised…
Feature-Aware (Hyper)graph Generation via Next-Scale Prediction
Dorian Gailhard, Enzo Tartaglione, Lirida Naviner +1
Graph generative models perform well on small-scale structured data but struggle to scale to large, complex structures. Hierarchical approaches improve scalability but often ignore…
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
Gabriele Spadaro, Alberto Presta, Jhony H. Giraldo +5
Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques mainly focus on high-fidelity reconstruction, re…
Piecewise Constant Spectral Graph Neural Network
Vahan Martirosyan, Jhony H. Giraldo, Fragkiskos D. Malliaros
Graph Neural Networks (GNNs) have achieved significant success across various domains by leveraging graph structures in data. Existing spectral GNNs, which use low-degree polynomia…
Continuous Simplicial Neural Networks
Aref Einizade, Dorina Thanou, Fragkiskos D. Malliaros +1
Simplicial complexes provide a powerful framework for modeling higher-order interactions in structured data, making them particularly suitable for applications such as trajectory p…
A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning
Amadou S. Sangare, Nicolas Dunou, Jhony H. Giraldo +1
Self-supervised learning has become a key method for training deep learning models when labeled data is scarce or unavailable. While graph machine learning holds great promise acro…
CATALOG: A Camera Trap Language-guided Contrastive Learning Model
Julian D. Santamaria, Claudia Isaza, Jhony H. Giraldo
Foundation Models (FMs) have been successful in various computer vision tasks like image classification, object detection and image segmentation. However, these tasks remain challe…
Variational Graph Contrastive Learning
Shifeng Xie, Jhony H. Giraldo
Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised…
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Jhony H. Giraldo, Aref Einizade, Andjela Todorovic +4
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-sca…
GABIC: Graph-based Attention Block for Image Compression
Gabriele Spadaro, Alberto Presta, Enzo Tartaglione +3
While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image Compression (LIC) codecs represent a promising alterna…
WiGNet: Windowed Vision Graph Neural Network
Gabriele Spadaro, Marco Grangetto, Attilio Fiandrotti +2
In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of…
OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
Muhammad Rameez Ur Rahman, Jhony H. Giraldo, Indro Spinelli +2
Event cameras, known for low-latency operation and superior performance in challenging lighting conditions, are suitable for sensitive computer vision tasks such as semantic segmen…
HYGENE: A Diffusion-based Hypergraph Generation Method
Dorian Gailhard, Enzo Tartaglione, Lirida Naviner +1
Hypergraphs are powerful mathematical structures that can model complex, high-order relationships in various domains, including social networks, bioinformatics, and recommender sys…
Privacy-Preserving Adaptive Re-Identification without Image Transfer
Hamza Rami, Jhony H. Giraldo, Nicolas Winckler +1
Re-Identification systems (Re-ID) are crucial for public safety but face the challenge of having to adapt to environments that differ from their training distribution. Furthermore,…
Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks
Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes i…
Continuous Product Graph Neural Networks
Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science. However, current methods are mostly limite…