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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 11011110 of 9051 papers

TitleStatusHype
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency LossesCode1
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot FillingCode1
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid MotionsCode1
C^2: Scalable Auto-Feedback for LLM-based Chart GenerationCode1
GenDexGrasp: Generalizable Dexterous GraspingCode1
Calliar: An Online Handwritten Dataset for Arabic CalligraphyCode1
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Barbie: Text to Barbie-Style 3D AvatarsCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
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