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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 38313840 of 9051 papers

TitleStatusHype
On Evaluation of Document Classification using RVL-CDIP0
From structure mining to unsupervised exploration of atomic octahedral networksCode0
Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast0
RSMT: Real-time Stylized Motion Transition for CharactersCode1
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation0
Spatial Heterophily Aware Graph Neural NetworksCode0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation0
Lingua Manga: A Generic Large Language Model Centric System for Data Curation0
Explicit Syntactic Guidance for Neural Text GenerationCode1
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