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 601–610 of 9051 papers

TitleStatusHype
SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite ImagesCode1
BehAVE: Behaviour Alignment of Video Game EncodingsCode1
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity RecognitionCode1
LLM Voting: Human Choices and AI Collective Decision MakingCode1
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
On the Affinity, Rationality, and Diversity of Hierarchical Topic ModelingCode1
ARGS: Alignment as Reward-Guided SearchCode1
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Learning High-Quality and General-Purpose Phrase RepresentationsCode1
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender SystemsCode1
Show:102550
← PrevPage 61 of 906Next →

No leaderboard results yet.