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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 3151–3175 of 9051 papers

TitleStatusHype
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—0
Enhancing Collective Intelligence in Large Language Models Through Emotional Integration—0
Enhancing Complex Formula Recognition with Hierarchical Detail-Focused Network—0
Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation—0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
Enhancing Diversity for Data-free Quantization—0
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers—0
Enhancing Diversity in Multi-objective Feature Selection—0
Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading—0
Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network—0
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation—0
Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies—0
Bregman Centroid Guided Cross-Entropy Method—0
Enhancing Facial Data Diversity with Style-based Face Aging—0
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies—0
Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques—0
3D Neural Field Generation using Triplane Diffusion—0
Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis—0
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes—0
Exploiting Web Images for Weakly Supervised Object Detection—0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling—0
Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis—0
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification—0
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