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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 1731–1740 of 9051 papers

TitleStatusHype
Atom Responding Machine for Dialog Generation—0
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification—0
Dynamic Latent Separation for Deep Learning—0
ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization—0
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization—0
A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector—0
Achieving Tight O(4^k) Runtime Bounds on Jump_k by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity—0
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
A Large Language Model for Feasible and Diverse Population Synthesis—0
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