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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 7776–7800 of 9051 papers

TitleStatusHype
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain AdaptationCode0
LawDNet: Enhanced Audio-Driven Lip Synthesis via Local Affine Warping DeformationCode0
Self-Paced Multi-Label Learning with DiversityCode0
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast AsiaCode0
Task-Agnostic Low-Rank Adapters for Unseen English DialectsCode0
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentationCode0
CrowdCounter: A benchmark type-specific multi-target counterspeech datasetCode0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Task Diversity Shortens the ICL PlateauCode0
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Cross-view Semantic Alignment for Livestreaming Product RecognitionCode0
Cross-Part Learning for Fine-Grained Image ClassificationCode0
Attribute Alignment: Controlling Text Generation from Pre-trained Language ModelsCode0
Personas with Attitudes: Controlling LLMs for Diverse Data AnnotationCode0
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?Code0
Educational Multi-Question Generation for Reading ComprehensionCode0
Leaping Into Memories: Space-Time Deep Feature SynthesisCode0
Task-Oriented Clustering for DialoguesCode0
Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence ModelCode0
Importance of Search and Evaluation Strategies in Neural Dialogue ModelingCode0
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node ImplantationCode0
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and DiversityCode0
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brainCode0
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