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George Pappas

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Articles

Computer Science > Artificial Intelligence

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By George Pappas, Hamed Hassani, Sima Noorani
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Computer Science > Machine Learning

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By George Pappas
| arXiv
[Submitted on 3 Apr 2026] Title:Adversarial Robustness of Deep State Space Models for Forecasting View a PDF of the paper titled Adversarial Robustness of Deep State Space Models for Forecasting, by Sribalaji C. Anand and George J. Pappas View PDF HTML (experimental) Abstract:State-space model (SSM) for time-series forecasting have demonstrated strong empirical performance on benchmark datasets, yet their robustness under adversarial perturbations is poorly understood.
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gmply

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By George Pappas
| eLearning Industry
What is gmply? GmPly is an interactive video learning platform designed to help organizations create engaging and measurable training experiences. The platform enables learning and development teams to produce interactive training videos that include quizzes, branching scenarios, and timed content elements. These features allow learners to actively participate in the training process instead of passively watching videos.
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