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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 28812890 of 9051 papers

TitleStatusHype
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity0
A Survey of Constraint Formulations in Safe Reinforcement Learning0
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?0
BehAVE: Behaviour Alignment of Video Game EncodingsCode1
An Empirical Analysis of Diversity in Argument Summarization0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation0
The effect of diversity on group decision-makingCode0
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents0
A fast density peak clustering based particle swarm optimizer for dynamic optimization0
Evolutionary Algorithms Simulating Molecular Evolution: A New Field Proposal0
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