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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 8701–8725 of 9051 papers

TitleStatusHype
Diversity-Robust Acoustic Feature Signatures Based on Multiscale Fractal Dimension for Similarity Search of Environmental Sounds—0
Diversity sampling is an implicit regularization for kernel methods—0
Diversity-Sensitive Conditional Generative Adversarial Networks—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
TARDiS : Text Augmentation for Refining Diversity and Separability—0
A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm—0
Diversity waves in collapse-driven population dynamics—0
Target Conditioning for One-to-Many Generation—0
Target Detection in OFDM-ISAC Systems: A Multipath Exploitation Approach—0
DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network—0
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction—0
Divide and Fuse: A Re-ranking Approach for Person Re-identification—0
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN—0
Targeted AMP generation through controlled diffusion with efficient embeddings—0
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks—0
Complexity measurement of natural and artificial languages—0
Complexity-based speciation and genotype representation for neuroevolution—0
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations—0
Targeted Augmentation for Low-Resource Event Extraction—0
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
Complementing cell taxonomies with a multicellular functional analysis of tissues—0
Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images—0
Complementary Recommendation in E-commerce: Definition, Approaches, and Future Directions—0
DMDD: A Large-Scale Dataset for Dataset Mentions Detection—0
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