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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 5201–5225 of 9051 papers

TitleStatusHype
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering—0
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography—0
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks—0
Self-supervised Human Mesh Recovery with Cross-Representation Alignment—0
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation—0
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images—0
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz—0
Self-supervised Representation Learning for Trip Recommendation—0
Self-Supervised Visual Acoustic Matching—0
Tunable Hybrid Proposal Networks for the Open World—0
Progressive Ensemble Networks for Zero-Shot Recognition—0
Self-Training for End-to-End Speech Recognition—0
Seller-side Outcome Fairness in Online Marketplaces—0
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts—0
Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review—0
Semantic Cells: Evolutional Process to Acquire Sense Diversity of Items—0
Semantic Diversity by Phonetics for Accurate and Robust Machine Translation—0
Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems—0
Semantic Diversity in Dialogue with Natural Language Inference—0
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector—0
Semantic Diversity versus Visual Diversity in Visual Dictionaries—0
Semantic Example Guided Image-to-Image Translation—0
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
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