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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 20412050 of 9051 papers

TitleStatusHype
DAEBAK!: Peripheral Diversity for Multilingual Word Sense Disambiguation0
DAFA: Diversity-Aware Feature Aggregation for Attention-Based Video Object Detection0
A Hybrid Frame Structure Design of OTFS for Multi-tasks Communications0
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning0
DAL: Dual Adversarial Learning for Dialogue Generation0
Daleel: Simplifying Cloud Instance Selection Using Machine Learning0
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction0
A small Griko-Italian speech translation corpus0
A Hybrid Bandit Framework for Diversified Recommendation0
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization0
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