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Found 11 papers, 1 papers with code
Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification
MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter.
A Sea-Land Clutter Classification Framework for Over-the-Horizon-Radar Based on Weighted Loss Semi-supervised GAN
The semi-supervised classification performance of WL-SSGAN is evaluated on a sea-land clutter dataset.
Data Augmentation and Classification of Sea-Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN
Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified.
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference
In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities.
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It
Limerick generation exemplifies some of the most difficult challenges faced in poetry generation, as the poems must tell a story in only five lines, with constraints on rhyme, stress, and meter.
Biomembranes undergo complex, non-axisymmetric deformations governed by Kirchhoff-Love kinematics and revealed by a three dimensional computational framework
Lipid bilayers are represented as spline-based surfaces immersed in a 3D space; this enables modeling of a wide spectrum of membrane geometries, boundary conditions, and deformations that are physically admissible in a 3D space.
Bayesian neural networks for weak solution of PDEs with uncertainty quantification
As both Dirichlet and Neumann BCs are specified as inputs to NNs, a single NN can solve for similar physics, but with different BCs and on a number of problem domains.
System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities.
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
To advance OFO, we propose an efficient online algorithm based on simultaneously learning a posterior probability of class and learning an optimal threshold by minimizing a stochastic strongly convex function with unknown strong convexity parameter.
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
In this paper, we consider statistical learning with AUC (area under ROC curve) maximization in the classical stochastic setting where one random data drawn from an unknown distribution is revealed at each iteration for updating the model.
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set.
