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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

Showing 11711180 of 9051 papers

TitleStatusHype
Conditional Image Synthesis With Auxiliary Classifier GANsCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Fractal Autoencoders for Feature SelectionCode1
Conditioned Query Generation for Task-Oriented Dialogue SystemsCode1
Image Disentanglement Autoencoder for Steganography Without EmbeddingCode1
Conditioned Text Generation with Transfer for Closed-Domain Dialogue SystemsCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Considering user agreement in learning to predict the aesthetic qualityCode1
FreEformer: Frequency Enhanced Transformer for Multivariate Time Series ForecastingCode1
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