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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 5601–5650 of 9051 papers

TitleStatusHype
Audio Generation with Multiple Conditional Diffusion Model—0
A Two-stage Evolutionary Framework For Multi-objective Optimization—0
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration—0
MixFeat: Mix Feature in Latent Space Learns Discriminative Space—0
Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition—0
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation—0
Mix-ME: Quality-Diversity for Multi-Agent Learning—0
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space—0
mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation—0
MixSyn: Learning Composition and Style for Multi-Source Image Synthesis—0
Transferable Query Selection for Active Domain Adaptation—0
Transfer Dynamics in Emergent Evolutionary Curricula—0
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning—0
Mixture of Experts in Large Language Models—0
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
Mixup Decoding for Diverse Machine Translation—0
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG—0
A two-stage algorithm in evolutionary product unit neural networks for classification—0
MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning—0
Transfer Learning by Modeling a Distribution over Policies—0
A Two-Layer Local Constrained Sparse Coding Method for Fine-Grained Visual Categorization—0
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct—0
A Tutorial On Intersectionality in Fair Rankings—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval—0
MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning—0
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification—0
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset—0
Attribute analysis with synthetic dataset for person re-identification—0
MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems—0
Attentive Aspect Modeling for Review-aware Recommendation—0
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation—0
Attention in Diffusion Model: A Survey—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning—0
Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis—0
Model-Based Quality-Diversity Search for Efficient Robot Learning—0
MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations—0
Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI—0
Transfer learning for ensembles: reducing computation time and keeping the diversity—0
Model Compression with Generative Adversarial Networks—0
Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy—0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans—0
Modeling cell differentiation in neuroblastoma: insights into development, malignancy, and treatment relapse—0
Modeling competitive evolution of multiple languages—0
Modeling Content Creator Incentives on Algorithm-Curated Platforms—0
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding—0
Modeling Cultural Dynamics—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder—0
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