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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 4601–4625 of 9051 papers

TitleStatusHype
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
Towards Building Text-To-Speech Systems for the Next Billion UsersCode2
Super-resolution Reconstruction of Single Image for Latent features—0
Availability, outage, and capacity of spatially correlated, Australasian free-space optical networks—0
GAMMT: Generative Ambiguity Modeling Using Multiple Transformers—0
Holographic Integrated Sensing and Communications: Principles, Technology, and Implementation—0
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node ImplantationCode0
Diversity and bias in audio captioning datasets—0
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
A Comparative Study of Question Answering over Knowledge BasesCode0
Local Magnification for Data and Feature Augmentation—0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity—0
Quality-diversity in dissimilarity spaces—0
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk DecodingCode1
Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation—0
Learning to Model Multimodal Semantic Alignment for Story Visualization—0
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent SpacesCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
VGFlow: Visibility guided Flow Network for Human Reposing—0
Quantifying syntax similarity with a polynomial representation of dependency treesCode0
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning—0
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling—0
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