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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 8526–8550 of 9051 papers

TitleStatusHype
Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion LexiconCode0
Visual Reasoning with Natural Language—0
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes—0
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN FrameworkCode0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications—0
Social Style Characterization from Egocentric Photo-streams—0
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue—0
Bayesian support for Evolution: detecting phylogenetic signal in a subset of the primate family—0
Query Completion Using Bandits for Engines Aggregation—0
Workflow Complexity for Collaborative Interactions: Where are the Metrics? -- A Challenge—0
Image Matching: An Application-oriented Benchmark—0
The Diverse Cohort Selection ProblemCode0
Opposition based Ensemble Micro Differential Evolution—0
Towards high-throughput 3D insect capture for species discovery and diagnostics—0
Exploring and Exploiting Diversity for Image Segmentation—0
Towards social pattern characterization in egocentric photo-streams—0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages—0
Differences in type-token ratio and part-of-speech frequencies in male and female Russian written texts—0
Neural Response Generation via GAN with an Approximate Embedding Layer—0
Affinity-Preserving Random Walk for Multi-Document Summarization—0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions—0
Multi-modular domain-tailored OCR post-correction—0
KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs—0
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