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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 7201–7225 of 9051 papers

TitleStatusHype
Data center top of rack switch to multiple spine switches optical wireless uplinks—0
Downlink and Uplink Intelligent Reflecting Surface Aided Networks: NOMA and OMA—0
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems—0
Dimensions of Diversity in Human Perceptions of Algorithmic Fairness—0
Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient—0
Data-dependent Gaussian Prior Objective for Language Generation—0
CTAP for Italian: Integrating Components for the Analysis of Italian into a Multilingual Linguistic Complexity Analysis Tool—0
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing—0
Prague Dependency Treebank - Consolidated 1.0—0
Gamification Platform for Collecting Task-oriented Dialogue Data—0
Introducing MULAI: A Multimodal Database of Laughter during Dyadic Interactions—0
Cifu: a Frequency Lexicon of Hong Kong Cantonese—0
CPLM, a Parallel Corpus for Mexican Languages: Development and Interface—0
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation—0
Morphological Segmentation for Low Resource Languages—0
Out-of-the-Box and into the Ditch? Multilingual Evaluation of Generic Text Extraction Tools—0
Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers—0
Scaling Language Data Import/Export with a Data Transformer Interface—0
Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?—0
When Ensembling Smaller Models is More Efficient than Single Large Models—0
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation—0
CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation—0
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution PerformanceCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Hybrid Adaptive Evolutionary Algorithm for Multi-objective Optimization—0
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