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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 5001–5025 of 9051 papers

TitleStatusHype
Resource and data efficient self supervised learning—0
Resource-Constrained Edge AI with Early Exit Prediction—0
Resource Mention Extraction for MOOC Discussion Forums—0
Resources for Multilingual Hate Speech Detection—0
Response of Different Tomato Accessions to Biotic and Abiotic Stresses—0
Restricting Greed in Training of Generative Adversarial Network—0
Rethinking Diversity in Deep Neural Network Testing—0
Rethinking and Refining the Distinct Metric—0
Rethinking Data Selection for Supervised Fine-Tuning—0
Rethinking Ecological Measures Of Functional Diversity—0
Rethinking Image Cropping: Exploring Diverse Compositions From Global Views—0
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY—0
Rethinking the Evaluation of Unbiased Scene Graph Generation—0
Rethinking Video Salient Object Ranking—0
Retinal Vessel Segmentation via Neuron Programming—0
Retinotopy Inspired Brain Encoding Model and the All-for-One Training Recipe—0
Retrievability in an Integrated Retrieval System: An Extended Study—0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies—0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies—0
Retrieval & Fine-Tuning for In-Context Tabular Models—0
Retrieval-guided Counterfactual Generation for QA—0
Retrieval-guided Counterfactual Generation for QA—0
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