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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 3551–3575 of 9051 papers

TitleStatusHype
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology—0
Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting—0
Facilitating Opinion Diversity through Hybrid NLP Approaches—0
Factor-Conditioned Speaking-Style Captioning—0
Consecutive Question Generation with Multitask Joint Reranking and Dynamic Rationale Search—0
Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems—0
Factorized Transformer for Multi-Domain Neural Machine Translation—0
Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability—0
Considerations for Ethical Speech Recognition Datasets—0
Fading Margins for Large-Scale Antenna Systems—0
FAID Diversity via Neural Networks—0
Failed Disruption Propagation in Integer Genetic Programming—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Diversified Late Acceptance Search—0
Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population—0
Bootstrapping NLP tools across low-resourced African languages: an overview and prospects—0
Consistency and Diversity induced Human Motion Segmentation—0
FAIR: Fairness-Aware Information Retrieval Evaluation—0
Diversified Hidden Markov Models for Sequential Labeling—0
Fairly Allocating Utility in Constrained Multiwinner Elections—0
An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization—0
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems—0
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets—0
Fairness and Diversity in Information Access Systems—0
Why Perturbing Symbolic Music is Necessary: Fitting the Distribution of Never-used Notes through a Joint Probabilistic Diffusion Model—0
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