NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
Unsupervised deep embedding for clustering analysis,
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.
NPCNet is a deep clustering model that converts EHR data to pseudo texts and uses a navigator module to align sepsis phenotypes with clinical knowledge.
A gradient manifold optimization method simultaneously learns a dimension reduction mapping and clusters the projected data under a GMM, reporting better results than standard clustering on MNIST.
citing papers explorer
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Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
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Language-Guided Multimodal Texture Authoring via Generative Models
A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.
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NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping
NPCNet is a deep clustering model that converts EHR data to pseudo texts and uses a navigator module to align sepsis phenotypes with clinical knowledge.
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Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization
A gradient manifold optimization method simultaneously learns a dimension reduction mapping and clusters the projected data under a GMM, reporting better results than standard clustering on MNIST.