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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 5276–5300 of 9051 papers

TitleStatusHype
Learning to Select Prototypical Parts for Interpretable Sequential Data ModelingCode0
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement LearningCode0
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
Towards Generating Diverse Audio Captions via Adversarial Training—0
Federated Neural Topic ModelsCode0
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation—0
Brain Tumor Synthetic Data Generation with Adaptive StyleGANsCode0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
Coevolutionary Framework for Generalized Multimodal Multi-objective OptimizationCode0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models—0
Emerging Diversity in a Population of Evolving Intransitive Dice—0
Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal StudyCode0
An Edge Alignment-based Orientation Selection Method for Neutron Tomography—0
To think inside the box, or to think out of the box? Scientific discovery via the reciprocation of insights and concepts—0
Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric LearningCode0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—0
ESG In Corporate Filings: An AI Perspective—0
3D Neural Field Generation using Triplane Diffusion—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene—0
Disentangled Generation with Information Bottleneck for Few-Shot Learning—0
DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model—0
Evaluating and reducing the distance between synthetic and real speech distributions—0
Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding—0
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