Francesco Ventura

Orcid: 0000-0003-3398-8265

According to our database1, Francesco Ventura authored at least 20 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Explaining deep convolutional models by measuring the influence of interpretable features in image classification.
Data Min. Knowl. Discov., September, 2024

2022
Trusting deep learning natural-language models via local and global explanations.
Knowl. Inf. Syst., 2022

Farming Your ML-based Query Optimizer's Food.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

DataFarm: Farm Your ML-based Query Optimizer's Food! - Human-Guided Training Data Generation -.
Proceedings of the 12th Conference on Innovative Data Systems Research, 2022

2021
Enhancing manufacturing intelligence through an unsupervised data-driven methodology for cyclic industrial processes.
Expert Syst. Appl., 2021

Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features.
CoRR, 2021

Expand your Training Limits! Generating Training Data for ML-based Data Management.
Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

2020
DSLE: A Smart Platform for Designing Data Science Competitions.
Proceedings of the 44th IEEE Annual Computers, Software, and Applications Conference, 2020

2019
What's in the box? Explaining the black-box model through an evaluation of its interpretable features.
CoRR, 2019

Automating concept-drift detection by self-evaluating predictive model degradation.
CoRR, 2019

Towards a real-time unsupervised estimation of predictive model degradation.
Proceedings of the International Workshop on Real-Time Business Intelligence and Analytics, 2019

A New Unsupervised Predictive-Model Self-Assessment Approach That SCALEs.
Proceedings of the 2019 IEEE International Congress on Big Data, 2019

PREMISES, a Scalable Data-Driven Service to Predict Alarms in Slowly-Degrading Multi-Cycle Industrial Processes.
Proceedings of the 2019 IEEE International Congress on Big Data, 2019

2018
iSTEP, an Integrated Self-Tuning Engine for Predictive Maintenance in Industry 4.0.
Proceedings of the IEEE International Conference on Parallel & Distributed Processing with Applications, 2018

Useful ToPIC: Self-Tuning Strategies to Enhance Latent Dirichlet Allocation.
Proceedings of the 2018 IEEE International Congress on Big Data, 2018

Black-Box Model Explained Through an Assessment of Its Interpretable Features.
Proceedings of the New Trends in Databases and Information Systems, 2018

2017
Data miners' little helper: data transformation activity cues for cluster analysis on document collections.
Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 2017

Prompting the data transformation activities for cluster analysis on collections of documents.
Proceedings of the 25th Italian Symposium on Advanced Database Systems, 2017

Self-tuning techniques for large scale cluster analysis on textual data collections.
Proceedings of the Symposium on Applied Computing, 2017

All in a twitter: Self-tuning strategies for a deeper understanding of a crisis tweet collection.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017


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