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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 6426–6450 of 9051 papers

TitleStatusHype
Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19Code1
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System—0
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
A Systematic Review of Reproducibility Research in Natural Language ProcessingCode0
Modelling Behavioural Diversity for Learning in Open-Ended GamesCode1
Meta Learning for Multi-agent Communication—0
Improving Diversity of Neural Text Generation via Inverse Probability Weighting—0
Treatment Effect Estimation using Invariant Risk MinimizationCode1
Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction—0
Conceptual capacity and effective complexity of neural networks—0
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face AssociationCode1
Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual InformationCode0
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning—0
Toward the Next Generation of News Recommender Systems—0
Topical Language Generation using TransformersCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization—0
Wide Aspect Ratio Matching for Robust Face Detection—0
Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning—0
Quantifying barley morphology using the Euler characteristic transform—0
Nondeterminism and Instability in Neural Network OptimizationCode0
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models—0
DI2: prior-free and multi-item discretization ofbiomedical data and its applicationsCode0
Adaptive Agent Architecture for Real-time Human-Agent Teaming—0
Representation Matters: Assessing the Importance of Subgroup Allocations in Training DataCode0
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