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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 4626–4650 of 9051 papers

TitleStatusHype
Evaluating the diversity and utility of materials proposed by generative models—0
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brainCode0
Social Media, Topic Modeling and Sentiment Analysis in Municipal Decision Support—0
A Comparative Study of Image-to-Image Translation Using GANs for Synthetic Child Race Data—0
Multimodal Color Recommendation in Vector Graphic Documents—0
Path Signatures for Diversity in Probabilistic Trajectory Optimisation—0
Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election—0
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration—0
FireFly A Synthetic Dataset for Ember Detection in WildfireCode0
Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG EvaluationCode0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling—0
Redundancy Aware Multi-Reference Based Gainwise Evaluation of Extractive Summarization—0
TSMD: A Database for Static Color Mesh Quality Assessment Study—0
A Taxonomy of Adaptive Traffic Signal Control—0
Feature-aware conditional GAN for category text generation—0
A Novel Cross-Perturbation for Single Domain Generalization—0
Are Easy Data Easy (for K-Means)—0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning—0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel OptimizationCode0
Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson ApproachesCode0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment—0
The Algonauts Project 2023 Challenge: UARK-UAlbany Team SolutionCode0
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