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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 48314840 of 9051 papers

TitleStatusHype
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model0
AARGH! End-to-end Retrieval-Generation for Task-Oriented DialogCode1
FedDAR: Federated Domain-Aware Representation Learning0
Text-Free Learning of a Natural Language Interface for Pretrained Face GeneratorsCode1
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine TranslationCode0
ESSYS* Sharing #UC: An Emotion-driven Audiovisual Installation0
Blessing of Class Diversity in Pre-training0
Open-Ended Evolution for Minecraft Building Generation0
Personalized Game Difficulty Prediction Using Factorization Machines0
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