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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 7251–7275 of 9051 papers

TitleStatusHype
A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm—0
Keyphrase Generation with Cross-Document AttentionCode1
Have you forgotten? A method to assess if machine learning models have forgotten data—0
The State and Fate of Linguistic Diversity and Inclusion in the NLP WorldCode0
Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric ModelCode1
Evolving Diverse Sets of Tours for the Travelling Salesperson Problem—0
On the Encoder-Decoder Incompatibility in Variational Text Modeling and BeyondCode0
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification—0
Exclusive Hierarchical Decoding for Deep Keyphrase GenerationCode1
Adaptive Multi-Receiver Coded Slotted ALOHA for Indoor Optical Wireless Communications—0
Diversity-based Design Assist for Large Legged Robots—0
Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery—0
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced DataCode0
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence GenerationCode0
AMPSO: Artificial Multi-Swarm Particle Swarm OptimizationCode0
WQT and DG-YOLO: towards domain generalization in underwater object detection—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
Personalized Re-ranking for Improving Diversity in Live Recommender Systems—0
Contrastive Examples for Addressing the Tyranny of the Majority—0
Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios—0
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling—0
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document SummarizationCode1
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation—0
BLEU might be Guilty but References are not InnocentCode1
ControlVAE: Controllable Variational Autoencoder—0
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