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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 6576–6600 of 9051 papers

TitleStatusHype
StyleFormer: Real-Time Arbitrary Style Transfer via Parametric Style CompositionCode1
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense KnowledgeCode1
Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering—0
Learning Disentangled Representations for Image Translation—0
BROS: A Pre-trained Language Model for Understanding Texts in Document—0
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
Robust Memory Augmentation by Constrained Latent Imagination—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
Tradeoffs in Data Augmentation: An Empirical Study—0
NOSE Augment: Fast and Effective Data Augmentation Without Searching—0
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity—0
A Reduction Approach to Constrained Reinforcement Learning—0
Ensemble-based Adversarial Defense Using Diversified Distance Mapping—0
KETG: A Knowledge Enhanced Text Generation Framework—0
CIGMO: Learning categorical invariant deep generative models from grouped data—0
On Nondeterminism and Instability in Neural Network Optimization—0
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning—0
Sample efficient Quality Diversity for neural continuous control—0
Deep Ensembles with Hierarchical Diversity PruningCode0
TaskSet: A Dataset of Optimization TasksCode0
Diversity Augmented Conditional Generative Adversarial Network for Enhanced Multimodal Image-to-Image TranslationCode0
Measuring GAN Training in Real Time—0
Learning a Non-Redundant Collection of Classifiers—0
Molecule Optimization by Explainable Evolution—0
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