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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 29012910 of 9051 papers

TitleStatusHype
Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning0
Diversity-based Design Assist for Large Legged Robots0
Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks0
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation0
Advances in Multi-turn Dialogue Comprehension: A Survey0
Enhancing Test Time Adaptation with Few-shot Guidance0
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models0
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch0
Enhancing the long-term performance of recommender system0
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving0
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