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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 8151–8175 of 9051 papers

TitleStatusHype
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences—0
Sequence to Sequence Mixture Model for Diverse Machine Translation—0
Automatic event detection in microblogs using incremental machine learning—0
Rotational 3D Texture Classification Using Group Equivariant CNNs—0
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement LearningCode0
Convex Hull Approximation of Nearly Optimal Lasso SolutionsCode0
Finding Similar Medical Questions from Question Answering Websites—0
Cats or CAT scans: transfer learning from natural or medical image source datasets?Code0
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor DiversityCode0
PointGrow: Autoregressively Learned Point Cloud Generation with Self-AttentionCode0
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of DocumentsCode0
Diffusion-like recommendation with enhanced similarity of objects—0
Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity—0
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases—0
Mind the GAP: A Balanced Corpus of Gendered Ambiguous PronounsCode0
A Family of Maximum Margin Criterion for Adaptive Learning—0
Toward Understanding the Impact of Staleness in Distributed Machine Learning—0
Deep Generative Video Compression—0
Adaptive, Personalized Diversity for Visual Discovery—0
Alibaba Submission to the WMT18 Parallel Corpus Filtering Task—0
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text GenerationCode0
Facts That MatterCode0
Similarity-Based Reconstruction Loss for Meaning Representation—0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations—0
Variational Autoregressive Decoder for Neural Response Generation—0
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