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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 2226–2250 of 9051 papers

TitleStatusHype
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member ModelsCode0
Capturing the diversity of multilingual societiesCode0
Growing Artificial Neural Networks for Control: the Role of Neuronal DiversityCode0
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
Grouping Words with Semantic DiversityCode0
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred LearningCode0
Group Relative Policy Optimization for Image CaptioningCode0
GridDehazeNet: Attention-Based Multi-Scale Network for Image DehazingCode0
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural NetworksCode0
Can Users Detect Biases or Factual Errors in Generated Responses in Conversational Information-Seeking?Code0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Gradient Estimators for Implicit ModelsCode0
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Hierarchical Pruning of Deep Ensembles with Focal DiversityCode0
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture ModelingCode0
A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic MeasuresCode0
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial NetworksCode0
GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANsCode0
Global Counterfactual DirectionsCode0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
GFlowNets and variational inferenceCode0
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