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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

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Showing 6651–6675 of 9051 papers

TitleStatusHype
Prim2Room: Layout-Controllable Room Mesh Generation from Primitives—0
Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones—0
PRIMO: Progressive Induction for Multi-hop Open Rule Generation—0
Universal Joy A Data Set and Results for Classifying Emotions Across Languages—0
Antithetic Noise in Diffusion Models—0
Priority Sampling of Large Language Models for Compilers—0
PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation—0
Priors for Diversity in Generative Latent Variable Models—0
Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning—0
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation—0
Privacy-Aware Crowd Labelling for Machine Learning Tasks—0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator—0
Privacy protection based on mask template—0
Private Synthetic Data Meets Ensemble Learning—0
Private Training Set Inspection in MLaaS—0
Probabilistic Fixed Ballot Rules and Hybrid Domains—0
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries—0
AntiBARTy Diffusion for Property Guided Antibody Design—0
Probabilistic Inference for Learning from Untrusted Sources—0
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
Universal point spread function engineering for 3D optical information processing—0
Antenna Array Calibration Via Gaussian Process Models—0
An Overview of the Research on Texture Based Plant Leaf Classification—0
Probabilistic Regressor Chains with Monte Carlo Methods—0
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs—0
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