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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 7076–7100 of 9051 papers

TitleStatusHype
Multi-sided Exposure Bias in RecommendationCode0
Design and Performance Analysis of a New STBC-MIMO LoRa System—0
Open Domain Suggestion Mining Leveraging Fine-Grained Analysis—0
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering—0
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study—0
Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes—0
Face-to-Music Translation Using a Distance-Preserving Generative Adversarial Network with an Auxiliary Discriminator—0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal TransportCode0
A Parameterized Family of Meta-Submodular Functions—0
SWAG: A Wrapper Method for Sparse Learning—0
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single SampleCode1
Wavelet Classification for Over-the-Air Non-Orthogonal Waveforms—0
A blindspot of AI ethics: anti-fragility in statistical prediction—0
Improving Query Safety at Pinterest—0
On the Theory of Transfer Learning: The Importance of Task Diversity—0
Semi-Discriminative Representation Loss for Online Continual LearningCode0
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial ExamplesCode0
Diverse Image Generation via Self-Conditioned GANsCode1
Learning High-Resolution Domain-Specific Representations with a GAN Generator—0
On the Robustness of Active Learning—0
Extraction and Evaluation of Formulaic Expressions Used in Scholarly Papers—0
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