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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

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Showing 4781–4790 of 9051 papers

TitleStatusHype
Parea: multi-view ensemble clustering for cancer subtype discoveryCode1
Start Small: Training Controllable Game Level Generators without Training Data by Learning at Multiple SizesCode0
Federated Stain Normalization for Computational PathologyCode0
Domain-Unified Prompt Representations for Source-Free Domain GeneralizationCode1
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in RecommendationCode0
Revisiting Few-Shot Learning from a Causal PerspectiveCode0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
Mutation Effect Generalizability under Selection-Drift—0
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual ExplanationsCode0
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