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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
Multiple Phase Transitions Shape Biodiversity of a Migrating Population—0
Multiple Population Alternate Evolution Neural Architecture Search—0
Multiple Realizability and the Rise of Deep Learning—0
Multi-population GAN Training: Analyzing Co-Evolutionary Algorithms—0
Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis—0
Multi-Resolution Generative Modeling of Human Motion from Limited Data—0
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant—0
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration—0
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution—0
Multi-scale Latent Point Consistency Models for 3D Shape Generation—0
Multi-scale Template Matching with Scalable Diversity Similarity in an Unconstrained Environment—0
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input—0
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems—0
Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification—0
Multi-Source Cross-Lingual Constituency Parsing—0
Multi-source Data Mining for e-Learning—0
Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios—0
Multi-Target Acquisition in Multistatic MIMO-OFDM Joint Sensing and Communication—0
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance—0
Multi-Task Determinantal Point Processes for Recommendation—0
Multi-task Learning-based Joint CSI Prediction and Predictive Transmitter Selection for Security—0
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography—0
Multi-Task Learning for Situated Multi-Domain End-to-End Dialogue Systems—0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases—0
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