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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 401410 of 9051 papers

TitleStatusHype
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Action detection using a neural network elucidates the genetics of mouse grooming behaviorCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
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