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 931940 of 9051 papers

TitleStatusHype
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
Evolving Flying Machines in Minecraft Using Quality DiversityCode1
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image RestorationCode1
A Large-scale Universal Evaluation Benchmark For Face Forgery DetectionCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Show:102550
← PrevPage 94 of 906Next →

No leaderboard results yet.