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Found 6 papers, 5 papers with code
Deconstructing the Goldilocks Zone of Neural Network Initialization
The second-order properties of the training loss have a massive impact on the optimization dynamics of deep learning models.
Unveiling the Hessian's Connection to the Decision Boundary
Based on this finding, we provide a new and straightforward approach to studying the complexity of a high-dimensional decision boundary; show that this connection naturally inspires a new generalization measure; and finally, we develop a novel margin estimation technique which, in combination with the generalization measure, precisely identifies minima with simple wide-margin boundaries.
Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence
Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions.
Unsupervised machine learning of topological phase transitions from experimental data
Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries also from noisy and imperfect data and without the knowledge of the order parameter.
Anomaly Detection
Quantum Gases
Disordered Systems and Neural Networks
Mesoscale and Nanoscale Physics
Quantum Physics
Magnetic properties and quench dynamics of two interacting ultracold molecules
We theoretically investigate the magnetic properties and nonequilibrium dynamics of two interacting ultracold polar and paramagnetic molecules in a one-dimensional harmonic trap in external electric and magnetic fields.
Atomic Physics Quantum Gases Chemical Physics Quantum Physics
Phase Detection with Neural Networks: Interpreting the Black Box
Neural networks (NNs) normally do not allow any insight into the reasoning behind their predictions.
Quantum Physics Disordered Systems and Neural Networks
