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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 6026–6050 of 9051 papers

TitleStatusHype
Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction—0
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation—0
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting—0
Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning—0
Unravelling Technical debt topics through Time, Programming Languages and Repository—0
UnSupDLA: Towards Unsupervised Document Layout Analysis—0
Unsupervised and Supervised Structure Learning for Protein Contact Prediction—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Unsupervised Data Augmentation for Less-Resourced Languages with no Standardized Spelling—0
Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation—0
Unsupervised Ensemble Learning with Dependent Classifiers—0
Unsupervised Features for Facial Expression Intensity Estimation over Time—0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
Unsupervised Hierarchical Story Infilling—0
Unsupervised inference approach to facial attractiveness—0
Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints—0
Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras—0
Unsupervised Multi-document Summarization with Holistic Inference—0
Unsupervised Paraphrasing by Simulated Annealing—0
Unsupervised Paraphrasing via Deep Reinforcement Learning—0
Unsupervised Segmentation of Multispectral Images with Cellular Automata—0
Unsupervised Separation of Transliterable and Native Words for Malayalam—0
Unsupervised Sign Language Phoneme Clustering using HamNoSys Notation—0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
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