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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 1451–1475 of 9051 papers

TitleStatusHype
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single SampleCode1
Diverse Image Generation via Self-Conditioned GANsCode1
Personalized Federated Learning with Moreau EnvelopesCode1
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Information Extraction of Clinical Trial Eligibility CriteriaCode1
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language ProcessingCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
Self-Distillation as Instance-Specific Label SmoothingCode1
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal AnglesCode1
Uncertainty quantification in medical image segmentation with normalizing flowsCode1
Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity MaximizationCode1
Bayesian Adversarial Human Motion SynthesisCode1
Controllable Multi-Interest Framework for RecommendationCode1
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
Multi-View Collaborative Network EmbeddingCode1
Learning Generalized Spoof Cues for Face Anti-spoofingCode1
TAGNN: Target Attentive Graph Neural Networks for Session-based RecommendationCode1
Neural Syntactic Preordering for Controlled Paraphrase GenerationCode1
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo MatchingCode1
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution PerformanceCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
New Protocols and Negative Results for Textual Entailment Data CollectionCode1
Self-Organized Operational Neural Networks with Generative NeuronsCode1
Multi-Objective Counterfactual ExplanationsCode1
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