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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 21012110 of 9051 papers

TitleStatusHype
Concept-skill Transferability-based Data Selection for Large Vision-Language ModelsCode1
GANmut: Generating and Modifying Facial Expressions0
STAR: Scale-wise Text-to-image generation via Auto-Regressive representationsCode2
Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation0
Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning0
Consistency-diversity-realism Pareto fronts of conditional image generative modelsCode2
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Linear Contextual Bandits with Hybrid Payoff: RevisitedCode0
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks0
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