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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 761770 of 9051 papers

TitleStatusHype
Controllable Group Choreography using Contrastive DiffusionCode1
F2GAN: Fusing-and-Filling GAN for Few-shot Image GenerationCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval AlgorithmsCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANsCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring TechniqueCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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