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 12411250 of 9051 papers

TitleStatusHype
Dan: Deep attention neural network for news recommendationCode1
Bias Loss for Mobile Neural NetworksCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Generating Highly Designable Proteins with Geometric Algebra Flow MatchingCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
Learning Diverse Risk Preferences in Population-based Self-playCode1
General Virtual Sketching Framework for Vector Line ArtCode1
Show:102550
← PrevPage 125 of 906Next →

No leaderboard results yet.