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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 7876–7900 of 9051 papers

TitleStatusHype
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition—0
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies—0
Minimum-Margin Active Learning—0
All-In-One Underwater Image Enhancement using Domain-Adversarial LearningCode0
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale AmbiguitiesCode1
Reinforcement Learning and Adaptive Sampling for Optimized DNN CompilationCode0
Nyström landmark sampling and regularized Christoffel functions—0
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial NetworksCode0
Semi-Implicit Generative Model—0
Spatial Evolutionary Generative Adversarial NetworksCode0
Semantic Diversity by Phonetics for Accurate and Robust Machine Translation—0
Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets—0
Insights into Complex Brain Functions Related to Schizophrenia Disorder through Causal Network Analysis—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Autonomous skill discovery with Quality-Diversity and Unsupervised DescriptorsCode0
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer SatisfactionCode0
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
High-Mobility Massive MIMO Communications:Doppler Compensation and Transmit Diversity—0
Mask-Guided Portrait Editing with Conditional GANs—0
Training language GANs from ScratchCode0
A COLD Approach to Generating Optimal Samples—0
Comparing and Combining Lexicase Selection and Novelty Search—0
Recurring Concept Meta-learning for Evolving Data Streams—0
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation—0
Explaining Machine Learning Classifiers through Diverse Counterfactual ExplanationsCode2
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