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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 24512460 of 9051 papers

TitleStatusHype
GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language UnderstandingCode0
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
Direct May Not Be the Best: An Incremental Evolution View of Pose GenerationCode0
An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-MakingCode0
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
Beyond BLEU: Training Neural Machine Translation with Semantic SimilarityCode0
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision TransformersCode0
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-TrainingCode0
Game Theory for Adversarial Attacks and DefensesCode0
"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?Code0
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