arxiv2d ago

Promoting Simple Agents: Ensemble Methods for Event-Log Prediction

arXiv:2604.21629v1 Announce Type: cross Abstract: We compare lightweight automata-based models (n-grams) with neural architectures (LSTM, Transformer) for next-activity prediction in streaming event logs. Experiments on synthetic patterns and five real-world process mining datasets show that n-grams

N-LSTR3 models #machine learning #ensemble methods #process mining

arxivApr 16

Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study

arXiv:2604.13928v1 Announce Type: new Abstract: Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods vali

ISTERE4 models · +1 #anomaly detection #industrial time-series #autoencoders Read on arxiv →

arxivApr 13

Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study

arXiv:2510.22293v4 Announce Type: replace Abstract: Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 30-40% of US adults and is the most common chronic liver disease. Although often asymptomatic, progression can lead to cirrhosis. The objective of the study was to

LARAXG5 models · +2 #machine learning #healthcare #prediction model Read on arxiv →

arxivApr 3

Semantic Interaction Information mediates compositional generalization in latent space

arXiv:2603.27134v2 Announce Type: replace Abstract: Are there still barriers to generalization once all relevant variables are known? We address this question via a framework that casts compositional generalization as a variational inference problem over latent variables with parametric interactions

REECFU4 models · +1 #machine learning #generalization #reinforcement learning Read on arxiv →

arxivApr 3bullish

Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control

arXiv:2603.19136v2 Announce Type: replace Abstract: Stock markets exhibit regime-dependent behavior where prediction models optimized for stable conditions often fail during volatile periods. Existing approaches typically treat all market states uniformly or require manual regime labeling, which is

AUDUSO3 models #machine learning #stock market #prediction Read on arxiv →

arxivApr 2

Event Embedding of Protein Networks : Compositional Learning of Biological Function

arXiv:2604.00911v1 Announce Type: new Abstract: In this work, we study whether enforcing strict compositional structure in sequence embeddings yields meaningful geometric organization when applied to protein-protein interaction networks. Using Event2Vec, an additive sequence embedding model, we trai

EVWO2 models #protein-protein interaction networks #sequence embeddings #machine learning Read on arxiv →