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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 8351–8375 of 9051 papers

TitleStatusHype
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework—0
Transferring GANs: generating images from limited dataCode0
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation—0
Unsupervised Features for Facial Expression Intensity Estimation over Time—0
Towards Interpretable Face RecognitionCode0
Introducing the CLARIN Knowledge Centre for Linguistic Diversity and Language Documentation—0
Towards faithfully visualizing global linguistic diversityCode0
We Are Depleting Our Research Subject as We Are Investigating It: In Language Technology, more Replication and Diversity Are Needed—0
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive StrategiesCode0
dhSegment: A generic deep-learning approach for document segmentationCode0
Customized Image Narrative Generation via Interactive Visual Question Generation and Answering—0
Domain Adaptation through Synthesis for Unsupervised Person Re-identification—0
Enhancing performance of coherent OTDR systems with polarization diversity complementary codes—0
Herding boosts too-connected-to-fail risk in stock market of China—0
On the effectiveness of task granularity for transfer learningCode1
MVTec D2S: Densely Segmented Supermarket Dataset—0
Generating Natural Language Adversarial ExamplesCode0
Progress and open problems in evolutionary dynamics—0
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity AlgorithmCode0
Application of Vector Sensor for Underwater Acoustic Communications—0
Personalized neural language models for real-world query auto completion—0
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory LearnersCode0
Data-efficient Neuroevolution with Kernel-Based Surrogate ModelsCode0
Asynch-SGBDT: Asynchronous Parallel Stochastic Gradient Boosting Decision Tree based on Parameters Server—0
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