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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 5326–5350 of 9051 papers

TitleStatusHype
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
Holographic Integrated Sensing and Communications: Principles, Technology, and Implementation—0
Super-resolution Reconstruction of Single Image for Latent features—0
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node ImplantationCode0
GAMMT: Generative Ambiguity Modeling Using Multiple Transformers—0
Availability, outage, and capacity of spatially correlated, Australasian free-space optical networks—0
Local Magnification for Data and Feature Augmentation—0
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity—0
A Comparative Study of Question Answering over Knowledge BasesCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Diversity and bias in audio captioning datasets—0
Quality-diversity in dissimilarity spaces—0
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation—0
Learning to Model Multimodal Semantic Alignment for Story Visualization—0
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
Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks—0
GANStrument: Adversarial Instrument Sound Synthesis with Pitch-invariant Instance Conditioning—0
Discord Questions: A Computational Approach To Diversity Analysis in News CoverageCode0
Content-Diverse Comparisons improve IQA—0
Synchronization of Diverse Agents via Phase Analysis—0
Active Learning with Tabular Language Models—0
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