Segev Shlomov

Orcid: 0000-0003-1216-8284

According to our database1, Segev Shlomov authored at least 18 papers between 2017 and 2024.

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Bibliography

2024
SNAP: Semantic Stories for Next Activity Prediction.
CoRR, 2024

Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition.
CoRR, 2023

Ongoing Tracking of Engagement in Motor Learning.
CoRR, 2023

Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges.
CoRR, 2023

2022
Understanding the Properties of Generated Corpora.
CoRR, 2022

Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration.
Proceedings of the Workshop on Process Management in the AI Era (PMAI 2022) co-located with 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022), 2022

Recommending Next Best Skill in Conversational Robotic Process Automation.
Proceedings of the Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum, 2022

2021
Robust learning in social networks via matrix scaling.
Oper. Res. Lett., 2021

Virtually additive learning.
J. Econ. Theory, 2021

We've had this conversation before: A Novel Approach to Measuring Dialog Similarity.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020
Statistical Significance Testing for Natural Language Processing
Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, ISBN: 978-3-031-02174-9, 2020

Do Not Have Enough Data? Deep Learning to the Rescue!
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Not Enough Data? Deep Learning to the Rescue!
CoRR, 2019

Robust Non-Bayesian Social Learning.
Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

Deep Dominance - How to Properly Compare Deep Neural Models.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018
The Hitchhiker's Guide to Testing Statistical Significance in Natural Language Processing.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017
Set-SMAA for finding preferable multi-objective solutions.
Proceedings of the Genetic and Evolutionary Computation Conference, 2017


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