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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 31913200 of 9051 papers

TitleStatusHype
GAIA: Zero-shot Talking Avatar Generation0
Vector-Quantized Prompt Learning for Paraphrase Generation0
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography0
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios0
CUCL: Codebook for Unsupervised Continual LearningCode0
Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation0
Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles0
Enhancing Task-Oriented Dialogues with Chitchat: a Comparative Study Based on Lexical Diversity and DivergenceCode0
Dialogue Quality and Emotion Annotations for Customer Support ConversationsCode0
Grammatical Error Correction via Mixed-Grained Weighted Training0
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