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Found 4 papers, 4 papers with code
Variational Data Assimilation with a Learned Inverse Observation Operator
Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data.
Homogeneous Linear Inequality Constraints for Neural Network Activations
One way to achieve this task is by means of a projection step at test time after unconstrained training.
Associative Domain Adaptation
Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain invariant embeddings, while minimizing the classification error on the labeled source domain.
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Domain Adaptation
on SYNSIG-to-GTSRB
Proximal Backpropagation
Specifically, we show that backpropagation of a prediction error is equivalent to sequential gradient descent steps on a quadratic penalty energy, which comprises the network activations as variables of the optimization.
