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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 7526–7550 of 9051 papers

TitleStatusHype
Boosting Semantic Segmentation from the Perspective of Explicit Class EmbeddingsCode0
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofingCode0
Attributed Graph Clustering via Adaptive Graph ConvolutionCode0
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
Instance-wise Supervision-level Optimization in Active LearningCode0
Attribute-aware Diversification for Sequential RecommendationsCode0
Data-efficient Neuroevolution with Kernel-Based Surrogate ModelsCode0
Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion RecognitionCode0
Orthogonal Ensemble Networks for Biomedical Image SegmentationCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple ReferencesCode0
Investigating Metric Diversity for Evaluating Long Document SummarisationCode0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented GuesserCode0
Analyzing the Dialect Diversity in Multi-document SummariesCode0
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational VideosCode0
Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text DetectionCode0
Active Learning for Regression Using Greedy SamplingCode0
SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing ImagesCode0
Boosting LLM via Learning from Data Iteratively and SelectivelyCode0
In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across LanguagesCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
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