1 citations · 1 across the 12 of their papers we have counts for
14 papers
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 message-passing operators, enabling effectively infinite-depth propagation, iteration-independ…
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…