Model fingerprint detection techniques have emerged as a promising approach for attributing AI-generated images to their source models, but their robustness und...
We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoenc...
Federated Learning (FL) distributes model training across clients who retain their data locally, but this architecture exposes a fundamental vulnerability: Byza...
Ensuring the safety of AI-enabled systems, particularly in high-stakes domains such as autonomous driving and healthcare, has become increasingly critical. Trad...
This paper presents a novel paradigm in simulation-based engineering sciences by introducing a new framework called Generative Parametric Design (GPD). The GPD ...
Spiking neural networks (SNNs), regarded as the third generation of artificial neural networks, are expected to bridge the gap between artificial intelligence a...
In this article, we rebuild Logistic Regression step by step directly in Excel. The post The Machine Learning “Advent Calendar” Day 12: Logistic Regression in E...
Recent advances in video analytics address real-time data drift by continuously retraining specialized, lightweight DNN models for individual cameras. However, ...
While voice-based AI systems have achieved remarkable generative capabilities, their interactions often feel conversationally broken. This paper examines the in...
Learning controller parameters from closed-loop data has been shown to improve closed-loop performance. Bayesian optimization, a widely used black-box and sampl...
Accurate modeling of spatiotemporal dynamics is crucial to understanding complex phenomena across science and engineering. However, this task faces a fundamenta...
