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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 4576–4600 of 9051 papers

TitleStatusHype
Improving Topic Coherence with Regularized Topic Models—0
Improving Topic Quality by Promoting Named Entities in Topic Modeling—0
Topological Data Analysis of Spatial Patterning in Heterogeneous Cell Populations: Clustering and Sorting with Varying Cell-Cell Adhesion—0
Topologically Robust 3D Shape Matching via Gradual Deflation and Inflation—0
Improving vision-language alignment with graph spiking hybrid Networks—0
Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages—0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing—0
IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems—0
Binary strings of finite VC dimension—0
Binary or nonbinary? An evolutionary learning approach to gender identity—0
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence—0
Incentive Mechanism Design for Distributed Ensemble Learning—0
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4—0
Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model—0
Topology-Aware Latent Diffusion for 3D Shape Generation—0
Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts—0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift—0
Leveraging LLMs for Influence Path Planning in Proactive Recommendation—0
Incorporating Dialectal Variability for Socially Equitable Language Identification—0
Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation—0
Increasing Data Diversity with Iterative Sampling to Improve Performance—0
Active Simultaneously Transmitting and Reflecting (STAR)-RISs: Modelling and Analysis—0
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions—0
Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification—0
Incremental Multi-graph Matching via Diversity and Randomness based Graph Clustering—0
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