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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 801810 of 9051 papers

TitleStatusHype
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
Curriculum-guided Hindsight Experience ReplayCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image ModelsCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
Are Large Language Models Capable of Generating Human-Level Narratives?Code1
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?Code1
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly DetectionCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial SensorsCode1
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