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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 29712980 of 9051 papers

TitleStatusHype
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity AlgorithmCode0
PEFT-U: Parameter-Efficient Fine-Tuning for User PersonalizationCode0
Event Transition Planning for Open-ended Text GenerationCode0
Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule GenerationCode0
Evaluator for Emotionally Consistent ChatbotsCode0
Accelerating Prototype-Based Drug Discovery using Conditional Diversity NetworksCode0
EventDrop: data augmentation for event-based learningCode0
Evidence for a multi-level trophic organization of the human gut microbiomeCode0
Evaluating Neural Language Models as Cognitive Models of Language AcquisitionCode0
AMPSO: Artificial Multi-Swarm Particle Swarm OptimizationCode0
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