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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 141150 of 9051 papers

TitleStatusHype
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian LanguagesCode2
NAVIX: Scaling MiniGrid Environments with JAXCode2
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive DiversityCode2
MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music GenerationCode2
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth EstimationCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation TasksCode2
UniGen: A Unified Framework for Textual Dataset Generation Using Large Language ModelsCode2
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