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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 921930 of 9051 papers

TitleStatusHype
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
Active Teacher for Semi-Supervised Object DetectionCode1
Lightweight Photometric Stereo for Facial Details RecoveryCode1
Lila: A Unified Benchmark for Mathematical ReasoningCode1
Lipschitz-constrained Unsupervised Skill DiscoveryCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
A Temporal Variational Model for Story GenerationCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
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