Explore a conference talk on advanced machine learning techniques for quantum systems, focusing on variational wavefunctions for fermions and gauge theories. Delve into the combination of machine learning architectures with physics-inspired approaches to build symmetry-preserving variational wave-functions. Examine the use of configuration-dependent Slater determinants for fermions and modified autoregressive neural networks for gauge theories. Discover how these methods lead to accurate results across various quantum systems, including larger systems and symmetry restoration. Gain insights into neural network flow, R by R matrix calculations, and slave fermion concepts in quantum mechanics.