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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 5226–5250 of 9051 papers

TitleStatusHype
Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models—0
Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition—0
Semantic-Rearrangement-Based Multi-Level Alignment for Domain Generalized Segmentation—0
Semantic Scene Completion with Multi-Feature Data Balancing Network—0
Semantics Disentangling for Text-to-Image Generation—0
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders—0
Semantics in Multi-objective Genetic Programming—0
Semantic uncertainty guides the extension of conventions to new referents—0
Semantic WordRank: Generating Finer Single-Document Summarizations—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Semi-Implicit Generative Model—0
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models—0
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition—0
Semi-supervised FusedGAN for Conditional Image Generation—0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion—0
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization—0
Sentence embedding with contrastive multi-views learning—0
Sentiment Classification in Swahili Language Using Multilingual BERT—0
SentiRec: Sentiment Diversity-aware Neural News Recommendation—0
Sequence to Sequence Mixture Model for Diverse Machine Translation—0
Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity—0
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control—0
Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering—0
Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation—0
Sequential Deformation for Accurate Scene Text Detection—0
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