graph neural networks

Deep Learning Applications for Circuit Optimization and Hardware Organization

Abstract Traditional Electronic Design Automation (EDA) logic synthesis struggles with NP-hard state-space explosions as hardware scales to billions of gates. This paper explores deep learning paradigms specifically Graph Neural Networks (GNNs), Reinforcement Learning (RL), and generative transformers to accelerate dig...

marimo-flow

Reactive marimo notebooks for ML experimentation, with MLflow tracking, PINA physics-informed neural networks, and a multi-agent team built on pydantic-graph and Ollama Cloud.

CHISA-RSI: Proof-Carrying Continuous Capability Closure for Recursive Self-Improving Networks - Global Stability-Plasticity, No-Valley Rewiring, and Certified Structural Self-Improvement

CHISA-RSI develops a self-contained mathematical framework for capability-preserving structural self-modification in finite modular computational networks. It extends the Chernoff-Hybrid Information Severance Algebra (CHISA) into a continuous theory of recursive self-improvement in which architectural changes can be gl...

RI Graph Sampling: A Hybrid Graph Sampling Method for Large-Scale Botnet Detection

Large-scale botnet detection using graph neural networks (GNNs) often requires subgraph sampling to reduce computational and memory costs. However, conventional sampling strategies may fail to simultaneously preserve the global topology and local community structure of the original graph, resulting in structural inform...

Few-Shot Welding Defect Detection via Graph Self-Attention Neural Network

In industrial quality control, deep learning-based weld defect detection has shown promise but is often hindered by the need for extensive labeled datasets. This study introduces a novel graph neural network (GNN) approach for few-shot classification of weld defect images, aimed at mitigating data dependence and enabli...

Enhancing structural to functional brain network prediction using topological graph learning

Understanding the relationship between structural and functional brain connectivity remains a fundamental challenge in network neuroscience. While structural connectivity constrains neural interactions, functional connectivity emerges from complex, nonlinear dynamics that are not fully explained by anatomical wirin...

Risk-Aware Hierarchical Meta-Reinforcement Learning with Quantile Regression LSTM Framework for Adaptive Task Prioritization and Congestion Avoidance Offloading in Fog–Cloud Systems

A graph network with a reinforcement learning framework to avoid congestion and schedule tasks with a low makespan in fog–IoT systems and demonstrates the improved ability to ensure reliable and effective fog–cloud allocation in response to dynamically changing workloads.

Behavior–Transaction Relation Dual-Branch Hybrid Graph Neural Network for Joint Modeling of Address Behavior and Transaction Relations in Illicit Bitcoin Address Detection

Grow with the Flow: Scalable Graph Neural Networks for Energy Grid Reliability

Exploring Engineers' Perspectives on the Adoption of Artificial Intelligence in Logic Circuit Design and Optimization

Abstract The increasing complexity of digital circuits has encouraged the use of Artificial Intelligence (AI) in Electronic Design Automation (EDA) to assist with logic synthesis, circuit optimization, and design analysis. Machine learning approaches, including Graph Neural Networks (GNNs) and Reinforcement Learning (R...

développement d’un GNN pour la reconnaissance faciale

Le rapport présente le développement d’une solution basée sur les Graph Neural Networks (GNN) pour l’exploitation de données géométriques. Après un état de l’art détaillant la représentation des données sous forme de graphes et les notions clés des GNN (message passing, mise à jour des nœuds, architectures GCN, GAT, Gr...

Prediction of organic additive-induced rheological enhancement of CTAB solutions using machine learning methods

Various organic additives can induce structural rearrangement of surfactant micelles in aqueous solutions, leading to enhanced rheological properties, namely an increase in viscosity, appearance of viscoelasticity, and non-Newtonian behavior. This phenomenon can be exploited in several industries to obtain strong gels....