Code, decontaminated data, and model weights for the manuscript "Chemical-space-aware routing enhances PFAS toxicity prediction: large-scale pretraining, conditional fine-tuning, and leakage control". The archive contains the complete pipeline for training and evaluating multi-task graph neural networks (Chemprop D-MPN...
Zhanting Yang· Zenodo (CERN European Organi...· 0 citations
We present a transferable graph neural network (GNN) surrogate framework for molecular dynamics (MD) that directly predicts atomic displacements and propagates atomistic configurations without explicit force evaluation or numerical time integration. The central objective of this work is to establish whether a common GN...
Graph Neural Network (GNN)-based intrusion detection systems (IDS) have emerged as powerful tools for modeling the structural patterns of network traffic. However, most existing methods rely on large, temporally aggregated graphs and random train-test splits, which risk information leakage from future traffic and overs...
Áron Kiss, K. Nehéz, O. Hornyák· Intelligent Data Analysis· 0 citations
Recent advances in artificial intelligence have transformed protein structure prediction and design. However, protein function is governed not only by static structures but also by the conformational dynamics that allow proteins to access distinct functional states. Predicting these dynamic transitions, central to many...
M. Marfoglia, Miguel A. Pedraza-Joya, L. Guirardel et al.· PLoS Biology· 0 citations
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The dominant approach to machine intelligence scales one architecture, the autoregressive transformer. A transformer is a fixed matrix of learned weights that produces output by statistical continuation. Four of its limits are structural, and adding parameters does not remove them: it has no internal test for truth, it...
Ibrahim Vandenberg· Zenodo (CERN European Organi...· 0 citations
A hierarchical Graph Neural Network (GNN) framework for ROI-level breast cancer subtype classification that represents nuclei and tissue regions as linked graph structures is presented and shows that sequential hierarchical fusion is the most effective configuration in this setting.
A. M. Rinaldi, Cristiano Russo, Cristian Tommasino· Data mining and knowledge di...· 0 citations
A dynamic graph neural network classification method integrating machine vision mapping and spatiotemporal evolution that effectively improves the generalization accuracy and anti-interference capability of heterogeneous network entity classification models.
Jing-Yi Xu· International Conference on...· 0 citations
The dominant approach to machine intelligence scales one architecture, the autoregressive transformer. A transformer is a fixed matrix of learned weights that produces output by statistical continuation. Four of its limits are structural, and adding parameters does not remove them: it has no internal test for truth, it...
Ibrahim Vandenberg· Zenodo (CERN European Organi...· 0 citations
This record provides supporting experimental data, source code, selected trained model weights, and reproducibility documentation for the manuscript “RART: A Resource-Aware Recursive Transformer for Stochastic Resource-Constrained Project Scheduling” by Cem Savas Aydin. The study evaluates RART on stochastic resource-c...
Cem Savas Aydin· Zenodo (CERN European Organi...· 0 citations
This record provides supporting experimental data, source code, selected trained model weights, and reproducibility documentation for the manuscript “RART: A Resource-Aware Recursive Transformer for Stochastic Resource-Constrained Project Scheduling” by Cem Savas Aydin. The study evaluates RART on stochastic resource-c...
Cem Savas Aydin· Zenodo (CERN European Organi...· 0 citations
Code, decontaminated data, and model weights for the manuscript "Chemical-space-aware routing enhances PFAS toxicity prediction: large-scale pretraining, conditional fine-tuning, and leakage control". The archive contains the complete pipeline for training and evaluating multi-task graph neural networks (Chemprop D-MPN...
Zhanting Yang· Zenodo (CERN European Organi...· 0 citations
Crystal graph neural networks predict materials properties by propagating information through local atomic environments. In conventional crystal graph convolutional neural networks (CGCNNs), this propagation depth is increased by stacking independently parameterized convolutional layers. This coupling between message-p...
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
