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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 10211030 of 9051 papers

TitleStatusHype
Efficient Dataset Distillation via Minimax DiffusionCode1
On the Role of Conceptualization in Commonsense Knowledge Graph ConstructionCode1
Few-shot Image Generation via Cross-domain CorrespondenceCode1
Bias Loss for Mobile Neural NetworksCode1
Biological Sequence Design with GFlowNetsCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring TechniqueCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
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