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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

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Showing 1701–1725 of 9051 papers

TitleStatusHype
Attribute analysis with synthetic dataset for person re-identification—0
A Learning Scheme for Microgrid Islanding and Reconnection—0
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity—0
DAL: Dual Adversarial Learning for Dialogue Generation—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
Daleel: Simplifying Cloud Instance Selection Using Machine Learning—0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment—0
Attentive Aspect Modeling for Review-aware Recommendation—0
Evaluations at Work: Measuring the Capabilities of GenAI in Use—0
Attention in Diffusion Model: A Survey—0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans—0
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
Attention-based Ensemble for Deep Metric Learning—0
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition—0
Dance Generation by Sound Symbolic Words—0
DarkBERT: A Language Model for the Dark Side of the Internet—0
Attempt Towards Stress Transfer in Speech-to-Speech Machine Translation—0
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
Abstractive and mixed summarization for long-single documents—0
Attacking Transformers with Feature Diversity Adversarial Perturbation—0
A trust-based recommendation method using network diffusion processes—0
DAFA: Diversity-Aware Feature Aggregation for Attention-Based Video Object Detection—0
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder—0
A tool for computing diversity and consideration on differences between diversity indices—0
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