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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 4621–4630 of 9051 papers

TitleStatusHype
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection—0
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation—0
Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation—0
Defining and Counting Phonological Classes in Cross-linguistic Segment Databases—0
Deflating Dataset Bias Using Synthetic Data Augmentation—0
Deformation Robust Text Spotting with Geometric Prior—0
DELIA: Diversity-Enhanced Learning for Instruction Adaptation in Large Language Models—0
Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model—0
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields—0
DeltaDQ: Ultra-High Delta Compression for Fine-Tuned LLMs via Group-wise Dropout and Separate Quantization—0
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