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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 2376–2400 of 9051 papers

TitleStatusHype
Knowledge-aware Text-Image Retrieval for Remote Sensing Images—0
The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning—0
Kinematic analysis of structural mechanics based on convolutional neural networkCode0
Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to EnglishCode0
Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental DesignCode0
How to Diversify any Personalized Recommender? A User-centric Pre-processing approach—0
Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems—0
Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention—0
Argumentative Large Language Models for Explainable and Contestable Claim VerificationCode1
Multi-Objective Recommendation via Multivariate Policy LearningCode0
Inherent Trade-Offs between Diversity and Stability in Multi-Task BenchmarksCode1
Exploring the Capabilities of Large Language Models for Generating Diverse Design SolutionsCode0
Layers of technology in pluriversal design. Decolonising language technology with the LiveLanguage initiative—0
Multi-Space Alignments Towards Universal LiDAR SegmentationCode2
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis ConstraintsCode2
Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration—0
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?—0
Benchmarking Representations for Speech, Music, and Acoustic EventsCode2
Generative manufacturing systems using diffusion models and ChatGPT—0
Multi-task Learning-based Joint CSI Prediction and Predictive Transmitter Selection for Security—0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business DocumentsCode1
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