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arXiv:2603.00053v1 Announce Type: new Abstract: Next Point-of-Interest (POI) recommendation is a critical task in location-based services, yet it faces the fundamental challenge of coupled spatiotemporal …
arXiv:2603.00054v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture is a powerful technique for scaling language models, yet it often suffers from expert homogenization, where …
arXiv:2603.00055v1 Announce Type: new Abstract: Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce …
arXiv:2603.00067v1 Announce Type: new Abstract: Medical time-series data are characterized by irregular sampling, high noise levels, missing values, and strong inter-feature dependencies. Recurrent neural networks …
arXiv:2603.00070v1 Announce Type: new Abstract: Standard evaluation metrics for machine learning -- accuracy, precision, recall, and AUROC -- assume that all errors are equivalent: a …
arXiv:2603.00099v1 Announce Type: new Abstract: Neural architecture search (NAS) automates the discovery of neural networks that meet specified criteria, yet its evaluation procedures are often …
arXiv:2603.00101v1 Announce Type: new Abstract: Wideband power amplifiers exhibit complex nonlinear and memory effects that challenge traditional behavioral modeling approaches. This paper proposes a novel …
arXiv:2603.00105v1 Announce Type: new Abstract: Large language models (LLMs) have gained significant attention by many researchers and practitioners in natural language processing (NLP) since the …
arXiv:2603.00137v1 Announce Type: new Abstract: Knowledge tracing (KT) models are commonly evaluated by training on early interactions from all students and testing on later responses. …
arXiv:2603.00176v1 Announce Type: new Abstract: Shared micromobility services such as e-scooters and bikes have become an integral part of urban transportation, yet their efficiency critically …
arXiv:2603.00180v1 Announce Type: new Abstract: Generative modeling of neural network parameters is often tied to architectures because standard parameter representations rely on known weight-matrix dimensions. …
arXiv:2603.00181v1 Announce Type: new Abstract: A recent report on "Learning the natural history of human disease with generative transformers" created an opportunity to assess the …