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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 13911400 of 9051 papers

TitleStatusHype
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic DiversityCode1
Parea: multi-view ensemble clustering for cancer subtype discoveryCode1
On the effectiveness of partial variance reduction in federated learning with heterogeneous dataCode1
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference OptimizationCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
HIVE: Evaluating the Human Interpretability of Visual ExplanationsCode1
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-IDCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
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