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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 611–620 of 9051 papers

TitleStatusHype
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Controllable Video Captioning with an Exemplar SentenceCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Show:102550
← PrevPage 62 of 906Next →

No leaderboard results yet.