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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 4726–4750 of 9051 papers

TitleStatusHype
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning—0
Pre-Optimized Irregular Arrays versus Moveable Antennas in Multi-User MIMO Systems—0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms—0
Preserving Product Fidelity in Large Scale Image Recontextualization with Diffusion Models—0
Presto! Distilling Steps and Layers for Accelerating Music Generation—0
Pre-Training BERT on Arabic Tweets: Practical Considerations—0
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
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
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
Probabilistic Inference for Learning from Untrusted Sources—0
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
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