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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 6351–6375 of 9051 papers

TitleStatusHype
Domain Generalization with MixStyle—0
Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction—0
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
Measuring Linguistic Diversity During COVID-19—0
Interpretable Unsupervised Diversity Denoising and Artefact RemovalCode1
Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation—0
Channel Estimation for MIMO Space Time Coded OTFS under Doubly Selective Channels—0
Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics—0
Situation-Specific Multimodal Feature Adaptation—0
Exploring Implicit Sentiment Evoked by Fine-grained News Events—0
Universal Joy A Data Set and Results for Classifying Emotions Across Languages—0
Abusive Language Recognition in RussianCode0
Implementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)—0
Comment Section Personalization: Algorithmic, Interface, and Interaction Design—0
No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems—0
IIIT_DWD@LT-EDI-EACL2021: Hope Speech Detection in YouTube multilingual comments—0
MUCS@LT-EDI-EACL2021:CoHope-Hope Speech Detection for Equality, Diversity, and Inclusion in Code-Mixed Texts—0
An Overview of Fairness in Data – Illuminating the Bias in Data Pipeline—0
Hopeful NLP@LT-EDI-EACL2021: Finding Hope in YouTube Comment Section—0
KU_NLP@LT-EDI-EACL2021: A Multilingual Hope Speech Detection for Equality, Diversity, and Inclusion using Context Aware Embeddings—0
Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion—0
cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model—0
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian LanguagesCode0
CFILT IIT Bombay@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion using Multilingual Representation fromTransformers—0
Simon @ LT-EDI-EACL2021: Detecting Hope Speech with BERT—0
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