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This paper introduces SCoR, a hierarchical framework for forecasting relations between scientific concepts, with a benchmark and model that improve research-direction discovery by predicting typed relations.
The paper introduces pFedHGP, a personalized federated learning approach using hierarchical Gaussian processes for probabilistic modeling of heterogeneous distributed systems while preserving privacy through federated variational inference.
The paper introduces a hybrid hierarchical 1D-CNN-BiLSTM framework for extractive summarization of biomedical and clinical text, designed to preserve factuality by selecting sentences directly from source documents rather than generating new text.
A hierarchical vision-language-action model improves long-horizon robot manipulation by using world-model-guided test-time search to scale computation for high-level subtask decisions.
This paper proposes HCIG, a hierarchical cross-modal incongruity graph network for multimodal sarcasm and cyberbullying detection, achieving state-of-the-art performance on the MMSD and MultiBully datasets.
HierBias introduces a hierarchical context-conditioned model for media bias detection that leverages document context to improve sentence-level classification, achieving state-of-the-art F1 and MCC on the BABE and BASIL datasets.
This paper presents DTVEM-RE, a hierarchical random-effects extension of the Differential Time-Varying Effect Model that estimates person-specific multi-lag coefficients via Hamiltonian Monte Carlo in Stan, addressing a limitation of the original DTVEM which assumed a single group-level lag structure. Simulation and empirical results demonstrate recovery of between-person variance and improvements over hierarchical and non-hierarchical baselines.