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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 7901–7950 of 9051 papers

TitleStatusHype
Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network—0
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data AugmentationCode0
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition—0
Parallel Optimal Transport GAN—0
Annotating and Characterizing Clinical Sentences with Explicit Why-QA CuesCode0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection—0
R3 Adversarial Network for Cross Model Face Recognition—0
Unsupervised Hierarchical Story Infilling—0
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar—0
Minimum-Margin Active Learning—0
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies—0
Reinforcement Learning and Adaptive Sampling for Optimized DNN CompilationCode0
All-In-One Underwater Image Enhancement using Domain-Adversarial LearningCode0
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial NetworksCode0
Spatial Evolutionary Generative Adversarial NetworksCode0
Nyström landmark sampling and regularized Christoffel functions—0
Semi-Implicit Generative Model—0
Semantic Diversity by Phonetics for Accurate and Robust Machine Translation—0
Insights into Complex Brain Functions Related to Schizophrenia Disorder through Causal Network Analysis—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer SatisfactionCode0
Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets—0
Autonomous skill discovery with Quality-Diversity and Unsupervised DescriptorsCode0
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
Mask-Guided Portrait Editing with Conditional GANs—0
High-Mobility Massive MIMO Communications:Doppler Compensation and Transmit Diversity—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
A Simple Dual-decoder Model for Generating Response with Sentiment—0
Recent Advances in Diversified Recommendation—0
A deep-learning-based approach for fast and robust steel surface defects classification—0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
Atom Responding Machine for Dialog Generation—0
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds—0
A novel statistical metric learning for hyperspectral image classification—0
Deep Sky Modeling for Single Image Outdoor Lighting Estimation—0
SISUA: Semi-Supervised Generative Autoencoder for Single Cell DataCode0
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks—0
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving—0
Argument Identification in Public Comments from eRulemaking—0
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling—0
MixFeat: Mix Feature in Latent Space Learns Discriminative Space—0
RelGAN: Relational Generative Adversarial Networks for Text GenerationCode0
Model Compression with Generative Adversarial Networks—0
Diverse Machine Translation with a Single Multinomial Latent Variable—0
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm—0
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