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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 6926–6950 of 9051 papers

TitleStatusHype
Recommenders with a mission: assessing diversity in newsrecommendations—0
Recommending Accurate and Diverse Items Using Bilateral Branch Network—0
Recommending on graphs: a comprehensive review from a data perspective—0
Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding—0
Reconciling the accuracy-diversity trade-off in recommendations—0
Reconfigurable Heterogeneous Parallel Island Models—0
Reconfigurable Holographic Surface-aided Distributed MIMO Radar Systems—0
Reconfigurable Holographic Surface: A New Paradigm to Implement Holographic Radio—0
Reconfigurable Intelligent Surface-Aided Grant-Free Access for Uplink URLLC—0
Reconfigurable Intelligent Surface Aided NOMA Networks—0
Reconfigurable Intelligent Surface-assisted Networks: Phase Alignment Categories—0
Reconfigurable Intelligent Surfaces and Machine Learning for Wireless Fingerprinting Localization—0
Reconstructing Objects in-the-wild for Realistic Sensor Simulation—0
Reconstructing seen images from human brain activity via guided stochastic search—0
Reconstructing Word Embeddings via Scattered k-Sub-Embedding—0
RECOST: External Knowledge Guided Data-efficient Instruction Tuning—0
Recoverability of Ancestral Recombination Graph Topologies—0
Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components—0
Rectified Flow For Structure Based Drug Design—0
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation—0
Recurrent and Contextual Models for Visual Question Answering—0
Unsupervised Multi-document Summarization with Holistic Inference—0
Recurring Concept Meta-learning for Evolving Data Streams—0
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization—0
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