Predicting the Future with Polynomial Networks

A new framework combines Hahn polynomials and Kolmogorov-Arnold networks for more accurate and efficient time series forecasting.

A new framework combines Hahn polynomials and Kolmogorov-Arnold networks for more accurate and efficient time series forecasting.

As artificial intelligence systems grow in complexity, pinpointing the source of errors becomes increasingly difficult, and this research introduces a new framework for statistically attributing failures to specific components.
A thought experiment explores how over-dependence on generative AI tools could subtly undermine core technical competencies and erode the foundations of trustworthy research in software engineering.

A new deep learning framework dramatically accelerates the prediction of blood flow dynamics within brain aneurysms, offering a faster path to risk assessment.
New research demonstrates that statistically rigorous economic modeling can rival the predictive power of machine learning in energy markets.

New research demonstrates how artificial intelligence can anticipate the likelihood of disclosed corporate risks becoming real-world problems.
New research explores the complex relationship between autistic individuals and AI chatbots like ChatGPT, revealing both empowering benefits and potential pitfalls.

A new reinforcement learning framework helps urban planners proactively design resilient transportation systems capable of weathering long-term climate change and minimizing disruption from flooding.

New research reveals that even highly capable AI systems can prioritize solving problems over recognizing and responding to urgent, real-world situations.
A new study analyzing 20,000 user interactions demonstrates how carefully designed AI systems can offer safer and more sensitive support for individuals grappling with mental health challenges.