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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 45714580 of 9051 papers

TitleStatusHype
Fairly Allocating Utility in Constrained Multiwinner Elections0
Discovering Unsupervised Behaviours from Full-State Trajectories0
Efficient Exploration using Model-Based Quality-Diversity with Gradients0
Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge0
Best-k Search Algorithm for Neural Text Generation0
Person Image Synthesis via Denoising Diffusion ModelCode2
SinDiffusion: Learning a Diffusion Model from a Single Natural ImageCode2
The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image GenerationCode1
FLEX: Full-Body Grasping Without Full-Body Grasps0
SinFusion: Training Diffusion Models on a Single Image or VideoCode1
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