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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 161170 of 9051 papers

TitleStatusHype
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and BeyondCode0
From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving0
Jailbreak Distillation: Renewable Safety Benchmarking0
Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation0
Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency DetectionCode1
AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion0
PoisonSwarm: Universal Harmful Information Synthesis via Model Crowdsourcing0
CNVSRC 2024: The Second Chinese Continuous Visual Speech Recognition Challenge0
LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion0
Conditional Diffusion Models with Classifier-Free Gibbs-like GuidanceCode0
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