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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 241250 of 9051 papers

TitleStatusHype
ReactDiff: Latent Diffusion for Facial Reaction GenerationCode0
SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs0
GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization0
The Effect of Language Diversity When Fine-Tuning Large Language Models for Translation0
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping0
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
Active Learning on Synthons for Molecular Design0
Few-Step Diffusion via Score identity Distillation0
EuLearn: A 3D database for learning Euler characteristicsCode0
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