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 241250 of 9051 papers

TitleStatusHype
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality EstimationCode2
Retrieval-Augmented Score Distillation for Text-to-3D GenerationCode2
ASpanFormer: Detector-Free Image Matching with Adaptive Span TransformerCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian LanguagesCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Show:102550
← PrevPage 25 of 906Next →

No leaderboard results yet.