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Found 8 papers, 0 papers with code
Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
We interpret these neural networks by finding an intersection between the equivalence class and human-readable equations defined by a symbolic search space.
Twin Neural Network Improved k-Nearest Neighbor Regression
Twin neural network regression is trained to predict differences between regression targets rather than the targets themselves.
How to get the most out of Twinned Regression Methods
Twinned regression methods are designed to solve the dual problem to the original regression problem, predicting differences between regression targets rather then the targets themselves.
Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries.
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present.
Twin Neural Network Regression
The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points.
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks
It turns out that in the process of learning which datapoints belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities.
Spectral Reconstruction with Deep Neural Networks
We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem.
