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arXiv:2602.22345v1 Announce Type: new Abstract: This thesis addresses two persistent and closely related challenges in modern deep learning, reliability and efficiency, through a unified framework …
arXiv:2602.22367v1 Announce Type: new Abstract: Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of the torso domain. The lead-field method enables fast …
arXiv:2602.22387v1 Announce Type: new Abstract: Biological signals of interest in high-dimensional data are often masked by dominant variation shared across conditions. This variation, arising from …
arXiv:2602.22400v1 Announce Type: new Abstract: The rise of Antimicrobial Resistance, particularly Multi-Drug Resistance (MDR), presents a critical challenge for clinical decision-making due to limited treatment …
arXiv:2602.22405v1 Announce Type: new Abstract: Most machine learning models for molecular property prediction rely on a single molecular representation (either a sequence, a graph, or …
arXiv:2602.22412v1 Announce Type: new Abstract: In matching markets such as kidney exchanges and freight exchanges, delayed matching has been shown to improve overall market efficiency. …
arXiv:2602.22422v1 Announce Type: new Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis. Their …
arXiv:2602.22428v1 Announce Type: new Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existing …
arXiv:2602.22438v1 Announce Type: new Abstract: Despite frequent double-blind review, systemic biases related to author demographics still disadvantage underrepresented groups. We start from a simple hypothesis: …
arXiv:2602.22446v1 Announce Type: new Abstract: Community detection in attributed networks faces a fundamental divide: topological algorithms ignore semantic features, while Graph Neural Networks (GNNs) encounter …
arXiv:2602.22470v1 Announce Type: new Abstract: Federated learning offers a privacy-friendly collaborative learning framework, yet its success, like any joint venture, hinges on the contributions of …
arXiv:2602.22479v1 Announce Type: new Abstract: Continual learning is a core requirement for deployed language models, yet standard training and fine-tuning pipelines remain brittle under non-stationary …