1 citations · 1 across the 12 of their papers we have counts for
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From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning
Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria Fodor
The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including sc…
Fixed Points Without Fixed Diffusion: Implicit Neural Sheaves for Convergent Test-Time Computation
Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria Fodor
Implicit Graph Neural Networks (IGNNs) define node representations as fixed points of graph neural operators, enabling effectively infinite-depth propagation, iteration-independent…
Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training
Ayush K. Varshney, Konstantinos Vandikas, Šarūnas Girdzijauskas +2
Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss. Quantization-Aware Training (QAT) has emerged as a…
Deep Neural Sheaf Diffusion
Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria Fodor
Deep Graph Neural Networks (GNNs) are essential for capturing complex dependencies in graph-structured data. However, scaling GNNs to depth remains challenging, as stacking layers…
Is One Token All It Takes? Graph Pooling Tokens for LLM-based GraphQA
Ankit Grover, Lodovico Giaretta, Rémi Bourgerie +1
The integration of Graph Neural Networks (GNNs) with Large Language Models (LLMs) has emerged as a promising paradigm for Graph Question Answering (GraphQA). However, effective met…
When to restart? Exploring escalating restarts on convergence
Ayush K. Varshney, Šarūnas Girdzijauskas, Konstantinos Vandikas +1
Learning rate scheduling plays a critical role in the optimization of deep neural networks, directly influencing convergence speed, stability, and generalization. While existing sc…
Automated Model Design using Gated Neuron Selection in Telecom
Adam Orucu, Marcus Medhage, Farnaz Moradi +2
The telecommunications industry is experiencing rapid growth in adopting deep learning for critical tasks such as traffic prediction, signal strength prediction, and quality of ser…