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2,177 papers
Detecting financial fraud in cryptocurrency networks like bitcoin is a significant challenge.The problem is that traditional machine learning models often fail to capture the complex relational patterns within transaction graphs, leading to poor detection.The purpose of this research is to evaluate hybrid models that i...
Aging infrastructure with limited budgets has driven stakeholders to explore new methods for monitoring structural health. Structural health monitoring represents a strategy to address this challenge using physical measurements to characterize performance. This approach has often leveraged various sensing modalities...
This paper provides a structured methodological review of intrusion detection systems (IDS) in the field of cybersecurity, with particular attention to Explainable Graph-Structured Reinforcement Learning (XGRL). Instead of presenting experimental evaluations, the study concentrates on analyzing design principles and me...
Product quality is crucial to the competitiveness of manufacturing enterprises. However, existing quality-aware scheduling studies mainly approximate product quality evolution using indirect factors such as processing time, making it difficult to characterize the integrated effects of equipment health conditions and op...
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Chapter 1: Machine Learning on Networks This chapter consists of three published papers, all developing and examining machine learning tools for graph-structured data.Graphs encode important structural properties of complex systems. Machine learning on graphs has therefore emerged as an important technique in research...
Abstract Predicting properties from coordinate–category data—sets of vectors paired with categorical information—is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been...
This work identifies two dimensions of diversity preferences—validated by psychological theory and readily inferable from interaction data and modeled using graph neighbor sampling: user preferences for diverse content across category levels, and preferences for fine-grained diversity within broad-level interests: user...
PolyNet is a Python package for polymer property prediction from (P)SMILES of homopolymers and copolymers. It trains graph neural networks and traditional machine-learning models on repeated train/validation/test splits, compares them statistically, explains their predictions (GNN fragment masking and SHAP), and predic...
Restored competence does not establish that a disturbance has been erased. A system may close an unresolved condition yet retain a causally effective distinction between histories that now share endpoint status and report. Motivated by Recovery Without Reset in the Experand adequacy framework, this paper applies classi...
Protein inverse folding, which infers compatible amino acid sequences from a given backbone structure, is central to rational protein design. Current methods rely largely on sequence recovery rate as the evaluation metric, emphasizing structural compatibility but seldom addressing function. Yet functional activity depe...
A regression model for the solubility of organic chemicals in pure water at 15 to 30 °C (log10 mol/L): the mean of gradient-boosted trees (XGBoost and LightGBM, five seeds each) and a message-passing graph neural network (Chemprop D-MPNN, five repeats, with the melting point as an auxiliary task). It was trained on 12...
The resurgence of the Mpox virus (MPXV) highlights the urgent need for scalable, mutation-resistant countermeasures. Slow development timelines hinder traditional empirical vaccine discovery, while conventional vaccinology often yields high false-positive rates because it relies on static, linear sequence-based screeni...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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.
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.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
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