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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 3026–3050 of 9051 papers

TitleStatusHype
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
A proposed new metric for the conceptual diversity of a text—0
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation—0
T cell receptor binding prediction: A machine learning revolution—0
SVGDreamer: Text Guided SVG Generation with Diffusion ModelCode2
Mesure simultanée des réponses impulsionnelles en macrodiversité—0
Towards Robust Multimodal Prompting With Missing Modalities—0
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3DCode1
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data LimitationsCode0
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs ResponsesCode1
BAL: Balancing Diversity and Novelty for Active LearningCode0
Natural Averaging May Complement Known Biological Constraints in Bi-parental Reproduction's Advantages Over Mono-parental in Conserving Species Quantitative Traits—0
Revisiting Knowledge Distillation under Distribution ShiftCode0
Diversity-Based Recruitment in Crowdsensing By Combinatorial Multi-Armed Bandits—0
Set Prediction Guided by Semantic Concepts for Diverse Video Captioning—0
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide GenerationCode1
Learning from diversity: jati fractionalization, social expectations and improved sanitation practices in India—0
TimePillars: Temporally-Recurrent 3D LiDAR Object Detection—0
Cross-Covariate Gait Recognition: A BenchmarkCode1
Quality-Diversity Generative Sampling for Learning with Synthetic DataCode1
Efficacy of Machine-Generated Instructions—0
De novo Drug Design using Reinforcement Learning with Multiple GPT AgentsCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?Code0
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion—0
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