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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 3301–3325 of 9051 papers

TitleStatusHype
Alibaba Submission to the WMT18 Parallel Corpus Filtering Task—0
CREA: A Collaborative Multi-Agent Framework for Creative Content Generation with Diffusion Models—0
CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation—0
A two-stage algorithm in evolutionary product unit neural networks for classification—0
CrashAgent: Crash Scenario Generation via Multi-modal Reasoning—0
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation—0
A Two-Layer Local Constrained Sparse Coding Method for Fine-Grained Visual Categorization—0
An Algorithm for Multi-Attribute Diverse Matching—0
CPLM, a Parallel Corpus for Mexican Languages: Development and Interface—0
A Tutorial On Intersectionality in Fair Rankings—0
COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Algorithmic Scenario Generation as Quality Diversity Optimization—0
A DAFT Based Unified Waveform Design Framework for High-Mobility Communications—0
A Case Study of User Communication Styles with Customer Service Agents versus Intelligent Virtual Agents—0
ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio—0
Dialog State Tracking with Reinforced Data Augmentation—0
Covariance Matrix Adaptation MAP-Annealing—0
Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval—0
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
Coupling in situ and remote sensing data to assess α- and β-diversity over biogeographic gradients—0
Counting and optimising maximum phylogenetic diversity sets—0
Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes—0
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization—0
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