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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 4901–4925 of 9051 papers

TitleStatusHype
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Semantic uncertainty guides the extension of conventions to new referents—0
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era—0
Template-based eukaryotic genome editing directed by SviCas3—0
Array-Informed Waveform Design for Active Sensing: Diversity, Redundancy, and Identifiability—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
On the Limitations of Model Stealing with Uncertainty Quantification Models—0
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection—0
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication—0
Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues—0
Creative Discovery using QD SearchCode0
Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system—0
Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning—0
Reinforcement Learning for Topic ModelsCode0
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer—0
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model—0
LayoutDM: Transformer-based Diffusion Model for Layout Generation—0
Experimental Validation of Coherent Joint Transmission in a Distributed-MIMO System with Analog Fronthaul for 6G—0
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization—0
Simple Noisy Environment Augmentation for Reinforcement LearningCode0
An Asynchronous Updating Reinforcement Learning Framework for Task-oriented Dialog SystemCode0
A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function—0
Considerations for Ethical Speech Recognition Datasets—0
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity AlignmentCode0
Multi-Task Multi-Behavior MAP-Elites—0
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