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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 8326–8350 of 9051 papers

TitleStatusHype
Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation—0
SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection—0
Diffusion Model with Perceptual Loss—0
Diffusion on language model encodings for protein sequence generation—0
Conformity bias in the cultural transmission of music sampling traditions—0
DiffusionPhase: Motion Diffusion in Frequency Domain—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
Configuring Antenna System to Enhance the Downlink Performance of High Velocity Users in 5G MU-MIMO Networks—0
ConfigTron: Tackling network diversity with heterogeneous configurations—0
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models—0
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Condorcet's Jury Theorem for Consensus Clustering and its Implications for Diversity—0
Digital Life Project: Autonomous 3D Characters with Social Intelligence—0
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules—0
DiMA: Sequence Diversity Dynamics Analyser for Viruses—0
DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models—0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets—0
Dimensions of Diversity in Human Perceptions of Algorithmic Fairness—0
SynPlay: Importing Real-world Diversity for a Synthetic Human Dataset—0
Syntactically Diverse Adversarial Network for Knowledge-Grounded Conversation Generation—0
From Intent Discovery to Recognition with Topic Modeling and Synthetic Data—0
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