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 11111120 of 9051 papers

TitleStatusHype
Can LLMs Patch Security Issues?Code1
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate SpeechCode1
Generating Highly Designable Proteins with Geometric Algebra Flow MatchingCode1
Can pre-trained models assist in dataset distillation?Code1
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation FrameworkCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency DetectionCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Show:102550
← PrevPage 112 of 906Next →

No leaderboard results yet.