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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 921930 of 9051 papers

TitleStatusHype
The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image GenerationCode1
Plug and Play Active Learning for Object DetectionCode1
SinFusion: Training Diffusion Models on a Single Image or VideoCode1
Automating Rigid Origami DesignCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationCode1
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot SummarizationCode1
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk DecodingCode1
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