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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 2311–2320 of 9051 papers

TitleStatusHype
Coherent Dialogue with Attention-based Language Models—0
Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States—0
Coherent and Archimedean choice in general Banach spaces—0
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—0
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels—0
A cross-study analysis of drug response prediction in cancer cell lines—0
Cognitive Learning-Aided Multi-Antenna Communications—0
CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation—0
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
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