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 731740 of 9051 papers

TitleStatusHype
Adversarial Parametric Pose PriorCode1
Co-Mixup: Saliency Guided Joint Mixup with Supermodular DiversityCode1
Domain-Unified Prompt Representations for Source-Free Domain GeneralizationCode1
DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language ModelsCode1
Effective Diversity in Population Based Reinforcement LearningCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arraysCode1
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-IDCode1
Show:102550
← PrevPage 74 of 906Next →

No leaderboard results yet.