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Found 6 papers, 5 papers with code
Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features
We investigate the test risk of continuous-time stochastic gradient flow dynamics in learning theory.
Extracting Blockchain Concepts from Text
Blockchains provide a mechanism through which mutually distrustful remote parties can reach consensus on the state of a ledger of information.
Learning curves for the multi-class teacher-student perceptron
For Gaussian teacher weights, we investigate the performance of ERM with both cross-entropy and square losses, and explore the role of ridge regularisation in approaching Bayes-optimality.
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent.
Restricted Boltzmann Machine Flows and The Critical Temperature of Ising models
We explore alternative experimental setups for the iterative sampling (flow) from Restricted Boltzmann Machines (RBM) mapped on the temperature space of square lattice Ising models by a neural network thermometer.
Age-structured estimation of COVID-19 ICU demand from low quality data
We sample aggravated cases following age-structured probabilities from confirmed cases and use ICU occupation data to find a subnotification factor.
Physics and Society Populations and Evolution
