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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 2551–2560 of 9051 papers

TitleStatusHype
CAViaR: Context Aware Video Recommendations—0
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation—0
Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning—0
Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering—0
Antithetic Noise in Diffusion Models—0
Causality-based Dual-Contrastive Learning Framework for Domain Generalization—0
Adversarial Learning of Semantic Relevance in Text to Image Synthesis—0
Do Neural Network Cross-Modal Mappings Really Bridge Modalities?—0
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains—0
Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing—0
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