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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 35413550 of 9051 papers

TitleStatusHype
On Performance of Fluid Antenna System using Maximum Ratio Combining0
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and CalibrationCode0
Masked Generative Modeling with Enhanced Sampling SchemeCode1
Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology ImagesCode1
Large-Vocabulary 3D Diffusion Model with TransformerCode1
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion EnhancementCode0
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition0
UnifiedGesture: A Unified Gesture Synthesis Model for Multiple SkeletonsCode1
A variational selection mechanism for article comment generationCode0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning0
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