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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 6976–7000 of 9051 papers

TitleStatusHype
Regularized directional representations for medical image registration—0
Regularized Submodular Maximization at Scale—0
Unsupervised Segmentation of Multispectral Images with Cellular Automata—0
A new selection strategy for selective cluster ensemble based on Diversity and Independency—0
Regularizing Dialogue Generation by Imitating Implicit Scenarios—0
Unsupervised Separation of Transliterable and Native Words for Malayalam—0
Regular Time-series Generation using SGM—0
Regurgitative Training: The Value of Real Data in Training Large Language Models—0
Unsupervised Sign Language Phoneme Clustering using HamNoSys Notation—0
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion—0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
Reinforcement-based frugal learning for satellite image change detection—0
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild—0
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices—0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords—0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
A BCS-GDE Algorithm for Multi-objective Optimization of Combined Cooling, Heating and Power Model—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
A New Repair Operator for Multi-objective Evolutionary Algorithm in Constrained Optimization Problems—0
A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function—0
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation—0
A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
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