Quality follows upgrading
Academic
arXiv:2603.19397v1 Announce Type: new Abstract: Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained …
arXiv:2603.19460v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance, but they often lack transparency. We introduce GeoLAN, a training framework that treats …
arXiv:2603.19463v1 Announce Type: new Abstract: We develop deep learning-based approximation methods for fully nonlinear second-order PDEs on separable Hilbert spaces, such as HJB equations for …
arXiv:2603.19465v1 Announce Type: new Abstract: We analyze a fixed-point iteration $v \leftarrow \phi(v)$ arising in the optimization of a regularized nuclear norm objective involving the …
arXiv:2603.19470v1 Announce Type: new Abstract: Off-policy problems such as policy staleness and training-inference mismatch, has become a major bottleneck for training stability and further exploration …
arXiv:2603.19474v1 Announce Type: new Abstract: High-quality GPS trajectories are essential for location-based web services and smart city applications, including navigation, ride-sharing and delivery. However, due …
arXiv:2603.19486v1 Announce Type: new Abstract: The inclusion of symmetries as an inductive bias, known as equivariance, often improves generalization on geometric data (e.g. grids, sets, …
arXiv:2603.19497v1 Announce Type: new Abstract: Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free …
arXiv:2603.19501v1 Announce Type: new Abstract: Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often …
arXiv:2603.19544v1 Announce Type: new Abstract: Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, …
arXiv:2603.19546v1 Announce Type: new Abstract: Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient. …
arXiv:2603.19562v1 Announce Type: new Abstract: Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with modality-specific …
