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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 4526–4550 of 9051 papers

TitleStatusHype
Data Representation and Compression Using Linear-Programming Approximations—0
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision—0
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing—0
Dataset Bias in Few-shot Image Recognition—0
Dataset Geography: Mapping Language Data to Language Users—0
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review—0
DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model—0
DATTA: Towards Diversity Adaptive Test-Time Adaptation in Dynamic Wild World—0
Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild—0
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition—0
DCAN: Diversified News Recommendation with Coverage-Attentive Networks—0
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning—0
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
DDS: A new device-degraded speech dataset for speech enhancement—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Decentralized Air Traffic Management for Advanced Air Mobility—0
Decentralized Collaborative Learning with Probabilistic Data Protection—0
Artificial Intelligence in Election Campaigns: Perceptions, Penalties, and Implications—0
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs—0
Decoding and Diversity in Machine Translation—0
Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics—0
Decoding the Diversity: A Review of the Indic AI Research Landscape—0
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