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Found 8 papers, 1 papers with code
Manifold Regularization for Memory-Efficient Training of Deep Neural Networks
Use of the framework results in improved absolute performance and empirical generalization error relative to traditional learning techniques.
Training Deep Normalizing Flow Models in Highly Incomplete Data Scenarios with Prior Regularization
Deep generative frameworks including GANs and normalizing flow models have proven successful at filling in missing values in partially observed data samples by effectively learning -- either explicitly or implicitly -- complex, high-dimensional statistical distributions.
Machine learning the real discriminant locus
This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions.
MCFlow: Monte Carlo Flow Models for Data Imputation
We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data.
Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis
Myotonia, which refers to delayed muscle relaxation after contraction, is the main symptom of myotonic dystrophy patients.
Towards Robust Deep Neural Networks
We investigate the topics of sensitivity and robustness in feedforward and convolutional neural networks.
The Loss Surface of XOR Artificial Neural Networks
Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters.
Deep Multimodal Representation Learning from Temporal Data
In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications.
