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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 721730 of 9051 papers

TitleStatusHype
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
Deep generative selection models of T and B cell receptor repertoires with soNNiaCode1
Effect of latent space distribution on the segmentation of images with multiple annotationsCode1
Efficient Dataset Distillation via Minimax DiffusionCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
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