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 321330 of 9051 papers

TitleStatusHype
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Dataset Factorization for CondensationCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Show:102550
← PrevPage 33 of 906Next →

No leaderboard results yet.