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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 431440 of 9051 papers

TitleStatusHype
Merging and Splitting Diffusion Paths for Semantically Coherent PanoramasCode1
Text3DAug -- Prompted Instance Augmentation for LiDAR PerceptionCode1
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
T3M: Text Guided 3D Human Motion Synthesis from SpeechCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Interpretable Long-term Action Quality AssessmentCode1
Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring TechniqueCode1
SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised PretrainingCode1
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
Barbie: Text to Barbie-Style 3D AvatarsCode1
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