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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 20512060 of 9051 papers

TitleStatusHype
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
A Small but Informed and Diverse Model: The Case of the Multimodal GuessWhat!? Guessing Game0
Dialogue Language Model with Large-Scale Persona Data Engineering0
Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models0
As long as you talk about me: The importance of family firm brands and the contingent role of family-firm identity0
Ask to Understand: Question Generation for Multi-hop Question Answering0
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection0
A Holistic Evaluation of Piano Sound Quality0
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