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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

Showing 421430 of 9051 papers

TitleStatusHype
AutoMix: Automatically Mixing Language ModelsCode1
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue DatasetCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient SpaceCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
Deep Ordinal Regression with Label DiversityCode1
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
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