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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

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Showing 1351–1360 of 9051 papers

TitleStatusHype
Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive VehiclesCode0
Tighter Performance Theory of FedExProx—0
Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method—0
Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Game—0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—0
On the Diversity of Synthetic Data and its Impact on Training Large Language Models—0
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling—0
Theoretical Aspects of Bias and Diversity in Minimum Bayes Risk DecodingCode0
An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-MakingCode0
mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code GenerationCode0
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