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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 3381–3390 of 9051 papers

TitleStatusHype
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model—0
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density—0
A Large Language Model for Feasible and Diverse Population Synthesis—0
Controlling Character Motions without Observable Driving Source—0
Controlling biases and diversity in diverse image-to-image translation—0
α-TCVAE: On the relationship between Disentanglement and Diversity—0
Controlled Randomness Improves the Performance of Transformer Models—0
A Taxonomy of Adaptive Traffic Signal Control—0
A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment—0
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