graph neural networks

Based on Multi-Modal Collaborative Learning for Latent Variable Discovery

This paper investigates the application of multi-modal collaborative learning for latent variable discovery. Traditional methods for uncovering hidden variables predominantly focus on single modality data, often overlooking the synergistic information present when multiple modalities are available. We propose a novel f...

Temporal Graph Neural Networks with Predictive Contextualization

This paper introduces Temporal Graph Neural Networks (TGNNs), a novel approach to graph neural networks designed to handle dynamic graphs where relationships evolve over time. Traditional Graph Neural Networks (GNNs) operate on static graphs, failing to capture the inherent temporal dependencies within systems. TGNNs a...

STOKES-DOT/GradTDDFT: GradTDDFT v1.1.0

GradTDDFT v1.1.0 GradTDDFT v1.1.0 provides a JAX-based framework for differentiable DFT, TDA/TDDFT, and neural exchange-correlation functional training. Highlights PySCF-aligned numerical grids: The standalone JAX grid now matches PySCF-style Lebedev quadrature, Treutler–Ahlrichs radial grids, NWChem pruning, and Becke...

Benchmarking predictive architectures for meningitis forecasting and outbreak detection using environmental drivers - code

This repository contains the analytical code for the manuscript, Benchmarking predictive architectures for meningitis forecasting and outbreak detection using environmental drivers. It provides the complete Python and PyTorch codebase to replicate our multi-architecture evaluation of early warning systems for epidemic...

Dynamic Semantic Embedding Network (DSE-Net)

This paper introduces the Dynamic Semantic Embedding Network (DSE-Net), a novel approach to understanding evolving data streams. The core claim is that by integrating semantic embeddings with dynamic graph neural networks, we can achieve continuous, context-aware understanding, overcoming the limitations of static embe...

Dynamic Topological Semantic Learning (DTSL)

This paper introduces Dynamic Topological Semantic Learning (DTSL), a novel approach to semantic understanding based on interactive observation and dynamic topological graph construction. The core claim of DTSL is that a system can automatically learn and build semantic relationships between objects by observing and si...

Accelerating Mixed Discrete-Continuous Motion Planning via Neural Graphs of Convex Sets

This work replaces the costly convex relaxation step required by nominal GCS with a single forward pass through a Graph Attention Network that predicts a set of highly probable candidate paths through the graph, and generates a lightweight ranking network that orders these candidates by their estimated trajectory cost.

TERE4AI: A Knowledge Graph-Based Tool for Generating EU AI Act Compliant Requirements

Machine Learning-Enhanced Tabu Search for Tactical Wireless Network Design

The proposed learning-assisted Tabu Search notably reduces computation time while consistently producing higher-quality solutions than the standard algorithm, highlighting the potential of combining machine learning with metaheuristics by leveraging the implicit knowledge embedded in search trajectories.

MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

A new graph neural network architecture with built-in meaningful per-atom attributions that produces attributions that are more faithful to chemical properties than other interpretability methods because the auxiliary loss in MolLedger anchors the atom scores to chemical properties.

Converse and Collision-Based Achievability for Node Localization with Hybrid Distance-Spectral Graph Positional Encodings

A hybrid distance-spectral encoding that combines anchor-distance profiles with quantized low-frequency Laplacian-energy coordinates is studied, showing that hybrid encodings better recover syntactic-tree geometry than distance-only or spectral-only baselines.

Exploring multi-answer visual question answering with object detection: a systematic review

This systematic literature review (SLR) focuses on multi answer VQA systems and the use of object detection, following the PRISMA 2020 guidelines and proposes a taxonomy of multi-answer VQA organized along four dimensions.