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 861870 of 9051 papers

TitleStatusHype
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration PathCode1
Controllable Multi-Interest Framework for RecommendationCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity RecognitionCode1
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language UnderstandingCode1
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Influence Selection for Active LearningCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Show:102550
← PrevPage 87 of 906Next →

No leaderboard results yet.