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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 221230 of 9051 papers

TitleStatusHype
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerceCode2
FLatten Transformer: Vision Transformer using Focused Linear AttentionCode2
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point TrackingCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Self-regulating Prompts: Foundational Model Adaptation without ForgettingCode2
UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction ServicesCode2
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion modelsCode2
StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar GenerationCode2
gRNAde: Geometric Deep Learning for 3D RNA inverse designCode2
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time SeriesCode2
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