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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 621630 of 9051 papers

TitleStatusHype
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior InferenceCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Diverse and Specific Clarification Question Generation with KeywordsCode1
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
Diverse Image Generation via Self-Conditioned GANsCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
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