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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 25812590 of 9051 papers

TitleStatusHype
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point ProcessesCode0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
Analyzing Uncertainty in Neural Machine TranslationCode0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
Boosting Deep Ensemble Performance with Hierarchical PruningCode0
DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine ModelCode0
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