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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 8251–8275 of 9051 papers

TitleStatusHype
Complete 3D Scene Parsing from an RGBD ImageCode0
Generating Language Corrections for Teaching Physical Control TasksCode0
Diversity By Design: Leveraging Distribution Matching for Offline Model-Based OptimizationCode0
Smoothing Entailment Graphs with Language ModelsCode0
Competition and Diversity in Generative AICode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
The Diversity-Innovation Paradox in ScienceCode0
A Simple, Fast Diverse Decoding Algorithm for Neural GenerationCode0
Diversity-Based Generalization for Unsupervised Text Classification under Domain ShiftCode0
A Comprehensive Evaluation on Event Reasoning of Large Language ModelsCode0
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AICode0
Provable and Efficient Continual Representation LearningCode0
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual LearningCode0
In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator LearningCode0
When Box Meets Graph Neural Network in Tag-aware RecommendationCode0
Comparison of Diverse Decoding Methods from Conditional Language ModelsCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image CaptioningCode0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
The Effect of Diversity in Meta-LearningCode0
Measuring the Diversity of Automatic Image DescriptionsCode0
Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent SubnetworksCode0
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced DataCode0
Generating Diverse Descriptions from Semantic GraphsCode0
Pruning Techniques for Mixed Ensembles of Genetic Programming ModelsCode0
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