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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 251–260 of 9051 papers

TitleStatusHype
UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models—0
Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds—0
Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image ClassificationCode1
Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation—0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans—0
Beyond Single-Point Judgment: Distribution Alignment for LLM-as-a-Judge—0
EvoGPT: Enhancing Test Suite Robustness via LLM-Based Generation and Genetic Optimization—0
Exploring Sparsity for Parameter Efficient Fine Tuning Using WaveletsCode0
WaLRUS: Wavelets for Long-range Representation Using SSMs—0
HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational PathologyCode2
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