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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 2561–2570 of 9051 papers

TitleStatusHype
Causal Conceptions of Fairness and their Consequences—0
5G Simulation-Based Experimentation Framework for Vertical Performance Assessment—0
Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis—0
CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation—0
Antenna Array Calibration Via Gaussian Process Models—0
Don't Throw Away Data: Better Sequence Knowledge Distillation—0
DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
D-PAGE: Diverse Paraphrase Generation—0
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