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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 7051–7075 of 9051 papers

TitleStatusHype
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image RecognitionCode1
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation—0
Abstractive and mixed summarization for long-single documents—0
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?Code0
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic SystemsCode0
A Case Study of User Communication Styles with Customer Service Agents versus Intelligent Virtual Agents—0
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
HausaMT v1.0: Towards English--Hausa Neural Machine TranslationCode1
Exploring Model Consensus to Generate Translation ParaphrasesCode0
Meeting the 2020 Duolingo Challenge on a Shoestring—0
Training and Inference Methods for High-Coverage Neural Machine Translation—0
English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs—0
Expand and Filter: CUNI and LMU Systems for the WNGT 2020 Duolingo Shared Task—0
Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
Long-Tail Predictions with Continuous-Output Language Models—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
Paraphrase Generation by Learning How to Edit from Samples—0
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer ModelsCode1
More Diverse Dialogue Datasets via Diversity-Informed Data Collection—0
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue GenerationCode1
How to Ask Good Questions? Try to Leverage Paraphrases—0
Language-aware Interlingua for Multilingual Neural Machine Translation—0
On the Importance of Diversity in Question Generation for QA—0
Deep Ordinal Regression with Label DiversityCode1
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