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arXiv:2603.00363v1 Announce Type: new Abstract: Distribution shifts in attack patterns within RPL-based IoT networks pose a critical threat to the reliability and security of large-scale …
arXiv:2603.00368v1 Announce Type: new Abstract: In this study, we present a meat freshness classification framework from Red-Green-Blue (RGB) images that supports both packaged and unpackaged …
arXiv:2603.00377v1 Announce Type: new Abstract: Full Waveform Inversion (FWI) is a highly nonlinear and ill-posed problem that aims to recover subsurface velocity maps from surface-recorded …
arXiv:2603.00396v1 Announce Type: new Abstract: Meta-Reinforcement Learning (Meta-RL) commonly generalizes via smoothness in the task encoding. While this enables local generalization around each training task, …
arXiv:2603.00397v1 Announce Type: new Abstract: Accurately solving time-dependent partial differential equations (PDEs) with neural networks remains challenging due to long-time error accumulation and the difficulty …
arXiv:2603.00404v1 Announce Type: new Abstract: In this study, a novel idea, Uncertainty Structure Estimation (USE), a lightweight, algorithm-agnostic procedure that emphasizes the often-overlooked role of …
arXiv:2603.00408v1 Announce Type: new Abstract: Deep neural networks (DNNs) enable high performance across domains but remain vulnerable to adversarial perturbations, limiting their use in safety-critical …
arXiv:2603.00417v1 Announce Type: new Abstract: Beyond binary classification, learnability can become a logically fragile notion: in EMX, even the class of all finite subsets of …
arXiv:2603.00425v1 Announce Type: new Abstract: Activation steering promises to be an extremely parameter-efficient form of adaptation, but its effectiveness depends on critical design choices -- …
arXiv:2603.00430v1 Announce Type: new Abstract: Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving …
arXiv:2603.00436v1 Announce Type: new Abstract: The need for machine unlearning is critical for data privacy, yet existing methods often cause Knowledge Contamination by unintentionally damaging …
arXiv:2603.00454v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) enable fine-tuning large language models to approximate reward-proportional posteriors, but they remain prone to mode collapse, …