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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 10711080 of 9051 papers

TitleStatusHype
Biological Sequence Design with GFlowNetsCode1
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide GenerationCode1
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object DetectionCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency LossesCode1
Few-Shot Video Object DetectionCode1
Bias Loss for Mobile Neural NetworksCode1
Adaptively Sparse TransformersCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
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