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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 18411850 of 9051 papers

TitleStatusHype
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Improving Generalization with Domain Convex GameCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Controllable Motion Generation via Diffusion Modal CouplingCode0
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Complex Locomotion Skill Learning via Differentiable PhysicsCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
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