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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 501510 of 9051 papers

TitleStatusHype
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Bacteriophage classification for assembled contigs using Graph Convolutional NetworkCode1
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion SegmentationCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning modelsCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
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