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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 36013610 of 9051 papers

TitleStatusHype
Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?0
Ensemble of Counterfactual ExplainersCode0
A Comprehensive Augmentation Framework for Anomaly Detection0
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning0
XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer SamplesCode0
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning0
Policy Diversity for Cooperative Agents0
FaceCoresetNet: Differentiable Coresets for Face Set RecognitionCode0
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series SummarizationCode0
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