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 10911100 of 9051 papers

TitleStatusHype
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency LossesCode1
FineRec:Exploring Fine-grained Sequential RecommendationCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
Fractal Autoencoders for Feature SelectionCode1
Adaptively Sparse TransformersCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
Bootstrapping Referring Multi-Object TrackingCode1
Biological Sequence Design with GFlowNetsCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
Show:102550
← PrevPage 110 of 906Next →

No leaderboard results yet.