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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 23112320 of 9051 papers

TitleStatusHype
XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT ScansCode0
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving0
MC-GPT: Empowering Vision-and-Language Navigation with Memory Map and Reasoning Chains0
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented DialoguesCode0
Diversity-Aware Sign Language Production through a Pose Encoding Variational Autoencoder0
Grounded 3D-LLM with Referent TokensCode2
Flow Score Distillation for Diverse Text-to-3D Generation0
Generative Design through Quality-Diversity Data Synthesis and Language Models0
SynthesizRR: Generating Diverse Datasets with Retrieval AugmentationCode1
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
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