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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 6901–6925 of 9051 papers

TitleStatusHype
Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space—0
Robust Deep Learning Ensemble against Deception—0
Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach—0
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language UnderstandingCode1
Fair and accurate age prediction using distribution aware data curation and augmentationCode0
Diversified Mutual Learning for Deep Metric Learning—0
Towards Unique and Informative Captioning of ImagesCode1
Improving Language Generation with Sentence Coherence ObjectiveCode0
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing—0
Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification—0
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services—0
Free-Space Optical MISO Communications With an Array of Detectors—0
Improved Weighted Random Forest for Classification Problems—0
Operational vs Convolutional Neural Networks for Image Denoising—0
Personalization in Human Activity Recognition—0
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequencesCode0
When Costly Punishment Becomes Evolutionarily Beneficial—0
Mapping Researchers with PeopleMapCode1
Unsupervised and Supervised Structure Learning for Protein Contact Prediction—0
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
A Framework for Improving Scholarly Neural Network Diagrams—0
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement LearningCode0
Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics—0
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