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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 6776–6800 of 9051 papers

TitleStatusHype
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism—0
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization—0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration—0
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML—0
Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization—0
Abstractive and mixed summarization for long-single documents—0
AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis—0
AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization—0
QoS-Aware 3D Coverage Deployment of UAVs for Internet of Vehicles in Intelligent Transportation—0
ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis—0
UnSupDLA: Towards Unsupervised Document Layout Analysis—0
Qualitative and Quantitative Analysis of Diversity in Cross-document Coreference Resolution Datasets—0
Qualitative Approaches to Voice UX—0
Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling—0
Quality and Diversity in Evolutionary Modular Robotics—0
Why SOV might be initially preferred and then lost or recovered? A theoretical framework—0
Annotation-Free MIDI-to-Audio Synthesis via Concatenative Synthesis and Generative Refinement—0
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization—0
Quality Diversity Evolutionary Learning of Decision Trees—0
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