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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 20012010 of 9051 papers

TitleStatusHype
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment0
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis0
A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions0
Multi-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching0
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented InterventionCode0
PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation TasksCode2
CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models0
Surrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimizationCode0
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