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 24712480 of 9051 papers

TitleStatusHype
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity0
ProTA: Probabilistic Token Aggregation for Text-Video Retrieval0
Enhance Robustness of Language Models Against Variation Attack through Graph Integration0
MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye trackingCode1
Evaluating AI for Law: Bridging the Gap with Open-Source Solutions0
IMIL: Interactive Medical Image Learning Framework0
The dynamics of diversity on corporate boards0
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem0
LMEraser: Large Model Unlearning through Adaptive Prompt TuningCode0
Show:102550
← PrevPage 248 of 906Next →

No leaderboard results yet.