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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 4301–4325 of 9051 papers

TitleStatusHype
Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approachCode0
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related TasksCode0
Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization—0
Generalization in medical AI: a perspective on developing scalable models—0
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data—0
Reconstructing Objects in-the-wild for Realistic Sensor Simulation—0
Workplace diversity and innovation performance: current state of affairs and future directions—0
MixTEA: Semi-supervised Entity Alignment with Mixture TeachingCode0
RankAug: Augmented data ranking for text classification—0
Assessing Distractors in Multiple-Choice Tests—0
CLearViD: Curriculum Learning for Video Description—0
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition—0
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation—0
AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems—0
Prediction of viral spillover risk based on the mass action principle—0
Bias and Diversity in Synthetic-based Face Recognition—0
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing UnderstandingCode0
Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking—0
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis—0
Measuring Adversarial Datasets—0
Tailoring Self-Rationalizers with Multi-Reward DistillationCode0
Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols—0
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance—0
LLMs grasp morality in concept—0
Perturbation-based Active Learning for Question Answering—0
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