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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 6076–6100 of 9051 papers

TitleStatusHype
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity—0
Using a sledgehammer to crack a nut? Lexical diversity and event coreference resolution—0
Using a thousand optimization tasks to learn hyperparameter search strategies—0
Using Audio Books for Training a Text-to-Speech System—0
Using Geometry to Rank Evenness Measures: Towards a Deeper Understanding of Divergence—0
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations—0
Using Known Words to Learn More Words: A Distributional Analysis of Child Vocabulary Development—0
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets—0
Using LLMs as prompt modifier to avoid biases in AI image generators—0
Using LSTMs for climate change assessment studies on droughts and floods—0
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain—0
Using novelty-biased GA to sample diversity in graphs satisfying constraints—0
Using Protected Attributes to Consider Fairness in Multi-Agent Systems—0
Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications—0
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation—0
Using Synthetic Images To Uncover Population Biases In Facial Landmarks Detection—0
Using the Overlapping Score to Improve Corruption Benchmarks—0
Utilizing Differential Evolution into optimizing targeted cancer treatments—0
Utilizing Large Language Models to Synthesize Product Desirability Datasets—0
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images—0
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model—0
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization—0
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors—0
Vagueness in Predicates and Objects—0
Validation Free and Replication Robust Volume-based Data Valuation—0
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