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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 6276–6300 of 9051 papers

TitleStatusHype
Participatory Budgeting with Donations and Diversity Constraints—0
Hyperspherically Regularized Networks for Self-Supervision—0
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks—0
Action Unit Memory Network for Weakly Supervised Temporal Action Localization—0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
The Future of Employment Revisited: How Model Selection Determines Automation Forecasts—0
Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation—0
Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem—0
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-BoundCode0
Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring ProblemCode0
LasHeR: A Large-scale High-diversity Benchmark for RGBT TrackingCode1
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space EmbeddingCode1
Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution—0
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary AlgorithmsCode0
Text Generation with Deep Variational GAN—0
Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation—0
Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study—0
Practical Wide-Angle Portraits Correction with Deep Structured ModelsCode1
CompOFA: Compound Once-For-All Networks for Faster Multi-Platform DeploymentCode1
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers—0
Vision Transformers with Patch DiversificationCode1
Less is more: Selecting informative and diverse subsets with balancing constraints—0
Shannon information criterion for low-high diversity transition in Moran and Voter models—0
Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity Modular RobotsCode0
LGD-GCN: Local and Global Disentangled Graph Convolutional NetworksCode1
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