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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 5076–5100 of 9051 papers

TitleStatusHype
Tackling covariate shift with node-based Bayesian neural networksCode1
Complex Locomotion Skill Learning via Differentiable PhysicsCode0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
(Im)possibility of Collective Intelligence—0
DCAN: Diversified News Recommendation with Coverage-Attentive Networks—0
Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset—0
Feature Space Particle Inference for Neural Network EnsemblesCode0
Long-tailed Recognition by Learning from Latent Categories—0
Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model—0
Construction of Responsive Utterance Corpus for Attentive Listening Response Production—0
AraSAS: The Open Source Arabic Semantic Tagger—0
COSMOS: Experimental and Comparative Studies of Concept Representations in Schoolchildren—0
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers—0
Universal Dependencies for Punjabi—0
Design and Evaluation of the Corpus of Everyday Japanese Conversation—0
MAKED: Multi-lingual Automatic Keyword Extraction Dataset—0
Creating a Basic Language Resource Kit for Faroese—0
Language Resources to Support Language Diversity – the ELRA Achievements—0
IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource—0
ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions—0
Language Resource Building and English-to-Mizo Neural Machine Translation Encountering Tonal Words—0
Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations—0
Overlooked Data in Typological Databases: What Grambank Teaches Us About Gaps in Grammars—0
The LTRC Hindi-Telugu Parallel Corpus—0
Multilingual Pragmaticon: Database of Discourse Formulae—0
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