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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 4151–4200 of 9051 papers

TitleStatusHype
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets—0
A Neural Approach to Pun Generation—0
BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph—0
Distributed Deep Learning for Modulation Classification in 6G Cell-Free Wireless Networks—0
On the Efficacy of Generalization Error Prediction Scoring Functions—0
GLDiTalker: Speech-Driven 3D Facial Animation with Graph Latent Diffusion Transformer—0
Image Segmentation via Divisive Normalization: dealing with environmental diversity—0
Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets—0
BigIssue: A Realistic Bug Localization Benchmark—0
Image Pre-processing on NumtaDB for Bengali Handwritten Digit Recognition—0
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport—0
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite—0
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure—0
Image Processing in Floriculture Using a robotic Mobile Platform—0
Distilling Diversity and Control in Diffusion Models—0
Big data analyses reveal patterns and drivers of the movements of southern elephant seals—0
Distance Shrinkage and Euclidean Embedding via Regularized Kernel Estimation—0
An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning—0
Image-Text Matching with Multi-View Attention—0
Impact of Interference on the Performance of RIS-Assisted Source DF Relaying Networks—0
An Entropy-based Measure of Intelligence Degree of System Structures—0
Dispersal-induced instability in complex ecosystems—0
A Design of A Simple Yet Effective Exercise Recommendation System in K-12 Online Learning—0
Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics—0
Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder—0
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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