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 26012610 of 9051 papers

TitleStatusHype
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
Boosting Semantic Segmentation from the Perspective of Explicit Class EmbeddingsCode0
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic HiringCode0
FireFly A Synthetic Dataset for Ember Detection in WildfireCode0
First the worst: Finding better gender translations during beam searchCode0
Analyzing the Habitable Zones of Circumbinary Planets Using Machine LearningCode0
Show:102550
← PrevPage 261 of 906Next →

No leaderboard results yet.