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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

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Showing 3471–3480 of 9051 papers

TitleStatusHype
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations—0
Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge—0
Constructing a meta-learner for unsupervised anomaly detection—0
A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data—0
Constraint-Based Software Diversification for Efficient Mitigation of Code-Reuse Attacks—0
Constraint-based Diversification of JOP Gadgets—0
Assessment of Practical Smart Gateway Diversity Based on Multi-Site Measurements in Q/V band—0
AID++: An Updated Version of AID on Scene Classification—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Abnormal Event Detection In Videos Using Deep Embedding—0
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