SOTAVerified

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 551560 of 9051 papers

TitleStatusHype
Multi-head Attention-based Deep Multiple Instance LearningCode1
Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin TonesCode1
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language ModelsCode1
WcDT: World-centric Diffusion Transformer for Traffic Scene GenerationCode1
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language ModelCode1
A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweetsCode1
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object InteractionsCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
Can 3D Vision-Language Models Truly Understand Natural Language?Code1
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