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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 2601–2625 of 9051 papers

TitleStatusHype
Scenarios Generation-based Multiple Interval Prediction Method for Electricity Prices—0
Capturing the diversity of biological tuning curves using generative adversarial networks—0
A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization—0
Capturing Bias Diversity in LLMs—0
Captioning Images Taken by People Who Are Blind—0
Distributionally-Informed Recommender System Evaluation—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
DVCFlow: Modeling Information Flow Towards Human-like Video Captioning—0
``Caption'' as a Coherence Relation: Evidence and Implications—0
CaptainGAN: Navigate Through Embedding Space For Better Text Generation—0
A Novel Mathematical Framework for Objective Characterization of Ideas—0
CapOnImage: Context-driven Dense-Captioning on Image—0
A Novel ILP Framework for Summarizing Content with High Lexical Variety—0
A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias—0
CAPA: Continuous-Aperture Arrays for Revolutionizing 6G Wireless Communications—0
A novel hybrid FSO / RF communication system with receive diversity—0
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?—0
A Novel Ensemble Learning Approach to Unsupervised Record Linkage—0
Adversarial Diversity and Hard Positive Generation—0
5G framework concepts for the next generation networks—0
Is News Recommendation a Sequential Recommendation Task?—0
A Novel Detection Algorithm Efficient for Turbo coded CDMA Signals in Detect and Forward Cooperative Channels—0
Adversarial Bootstrapping for Dialogue Model Training—0
Can time series forecasting be automated? A benchmark and analysis—0
Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?—0
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