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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 4176–4200 of 9051 papers

TitleStatusHype
Bias-Variance Decompositions for Margin Losses—0
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification—0
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation—0
BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly—0
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines—0
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
An ensemble diversity approach to supervised binary hashing—0
Image Data Augmentation for Deep Learning: A Survey—0
Disentangled Generation with Information Bottleneck for Few-Shot Learning—0
Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset—0
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring—0
Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors—0
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias—0
An Enhancement of Jiang, Z., et al.s Compression-Based Classification Algorithm Applied to News Article Categorization—0
Discursive objection strategies in online comments: Developing a classification schema and validating its training—0
Biased Random-Key Genetic Algorithms: A Review—0
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning—0
Discriminative Active Learning for Domain Adaptation—0
Bias, diversity, and challenges to fairness in classification and automated text analysis. From libraries to AI and back—0
An Energy Activity Dataset for Smart Homes—0
Discrete Structural Planning for Generating Diverse Translations—0
Bias Begets Bias: The Impact of Biased Embeddings on Diffusion Models—0
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Discrepancy-based Evolutionary Diversity Optimization—0
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