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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

Showing 40114020 of 9051 papers

TitleStatusHype
DiffCap: Exploring Continuous Diffusion on Image Captioning0
Self-QA: Unsupervised Knowledge Guided Language Model AlignmentCode3
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction0
Few-shot 3D Shape Generation0
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition0
Evolutionary Diversity Optimisation in Constructing Satisfying Assignments0
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis0
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time SeriesCode2
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language ModelsCode2
Learning Diverse Risk Preferences in Population-based Self-playCode1
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