Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Explores Car-Parrinello molecular dynamics, a unified approach combining molecular dynamics and density-functional theory for simulating various systems, with a focus on historical background, technical details, and challenges in atomistic simulations.
Explores the EXSCALATE4COV project, focusing on computational drug discovery for COVID-19 treatments and the collaboration between academia and industry.
Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.
Explores Computational Molecular Design, focusing on Mathematical Theory, High Performance Computing, and In Vivo Experiments, with an emphasis on quantum chemistry and electron dynamics.
