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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 7501–7525 of 9051 papers

TitleStatusHype
Ensembles of Locally Independent Prediction ModelsCode0
Auditing for Diversity using Representative ExamplesCode0
Ensemble Pruning based on Objection Maximization with a General Distributed FrameworkCode0
Interactive Image Segmentation With Latent DiversityCode0
Optimally Combining Classifiers for Semi-Supervised LearningCode0
BooVAE: Boosting Approach for Continual Learning of VAECode0
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question AnsweringCode0
Ensemble of heterogeneous flexible neural trees using multiobjective genetic programmingCode0
Data Fusion for Deep Learning on Transport Mode Detection: A Case StudyCode0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
Toward Robust Uncertainty Estimation with Random Activation FunctionsCode0
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language ModelsCode0
Intentional Computational Level DesignCode0
Optimization Variance: Exploring Generalization Properties of DNNsCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
Ensemble of Counterfactual ExplainersCode0
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing DataCode0
Integrating Present and Past in Unsupervised Continual LearningCode0
Intrinsically-Motivated Humans and Agents in Open-World ExplorationCode0
Attribute Diversity Determines the Systematicity Gap in VQACode0
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
Optimizing Keyphrase Ranking for Relevance and Diversity Using Submodular Function Optimization (SFO)Code0
Data-free Knowledge Distillation for Segmentation using Data-Enriching GANCode0
Ensemble Distribution DistillationCode0
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model MarketCode0
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