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arXiv:2603.11355v1 Announce Type: new Abstract: We introduce Teleodynamic Learning, a new paradigm for machine learning in which learning is not the minimization of a fixed …
arXiv:2603.11358v1 Announce Type: new Abstract: Financial fraud detection has emerged as a critical research challenge amid the rapid expansion of digital financial platforms. Although machine …
arXiv:2603.11369v1 Announce Type: new Abstract: Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this …
arXiv:2603.11372v1 Announce Type: new Abstract: Mechanical ventilation (MV) is a life-saving intervention for patients with acute respiratory failure (ARF) in the ICU. However, inappropriate ventilator …
arXiv:2603.11395v1 Announce Type: new Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance …
arXiv:2603.11396v1 Announce Type: new Abstract: Manifold learning is a fundamental task at the core of data analysis and visualisation. It aims to capture the simple …
arXiv:2603.11428v1 Announce Type: new Abstract: Statistical dependence measures like mutual information is ideal for analyzing autoencoders, but it can be ill-posed for deterministic, static, noise-free …
arXiv:2603.11436v1 Announce Type: new Abstract: This study addresses the challenge of automatically detecting semantic column types in relational tables, a key task in many real-world …
arXiv:2603.11456v1 Announce Type: new Abstract: Unsupervised neural combinatorial optimization (NCO) offers an appealing alternative to supervised approaches by training learning-based solvers without ground-truth solutions, directly …
arXiv:2603.11462v1 Announce Type: new Abstract: Predicting irregularly spaced event sequences with discrete marks poses significant challenges due to the complex, asynchronous dependencies embedded within continuous-time …
arXiv:2603.11473v1 Announce Type: new Abstract: Nonlinear Probabilistic Latent Variable Models (NPLVMs) are a cornerstone of soft sensor modeling due to their capacity for uncertainty delineation. …
arXiv:2603.11475v1 Announce Type: new Abstract: Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine …