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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 4631–4640 of 9051 papers

TitleStatusHype
Delving into Rectifiers in Style-Based Image Translation—0
Delving into Transformer for Incremental Semantic Segmentation—0
DEM: Distribution Edited Model for Training with Mixed Data Distributions—0
Democratizing AI Governance: Balancing Expertise and Public Participation—0
Demographic Attributes Prediction from Speech Using WavLM Embeddings—0
Demonstration Selection for In-Context Learning via Reinforcement Learning—0
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance—0
Denoising Attention for Query-aware User Modeling in Personalized Search—0
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols—0
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