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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 8826–8850 of 9051 papers

TitleStatusHype
Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
Teaching to Teach by Structured Dark Knowledge—0
DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance—0
Comment Section Personalization: Algorithmic, Interface, and Interaction Design—0
Is there sufficient evidence for criticality in cortical systems?—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models—0
V-VIPE: Variational View Invariant Pose Embedding—0
DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization—0
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning—0
Technical Report: Competition Solution For BetterMixture—0
DUM: Diversity-Weighted Utility Maximization for Recommendations—0
Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning—0
DVCFlow: Modeling Information Flow Towards Human-like Video Captioning—0
Comeback kids: an evolutionary approach of the long-run innovation process—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Dynamical Isometry for Residual Networks—0
Dynamically evolving segment anything model with continuous learning for medical image segmentation—0
Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network—0
Teleconnection patterns of different El Niño types revealed by climate network curvature—0
Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features—0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
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