Abstract: A computer-implemented system and method for training a neural network with enforced monotonicity are disclosed. An example system includes at least one processor and memory in communication with said at least one processor, wherein the memory stores instructions for providing a data model representing a neural network for predicting an outcome based on input data, the instructions when executed at said at least one processor causes said system to: receive a feature data as input data; predict an outcome based on the input data using the neural network; compute a loss function based on the predicted outcome and an expected outcome associated with the input data, the loss function being dependent on a monotonicity penalty ? computed based on a set of training data including the feature data and on a set of random data; and update weights of the neural network based on the loss function.

Abstract: Systems and methods of generating interpretive data associated with data sets. Embodiments of systems may be for adapting Grad-CAM methods for embedding networks. The system includes a processor and a memory. The memory stores processor-executable instructions that, when executed, configure the processor to: obtain a subject data set; generate a feature embedding based on the subject data set; determine an embedding gradient weight based on a prior-trained embedding network and the feature embedding associated with the subject data set, the prior-trained embedding network defined based on a plurality of embedding gradient weights respectively corresponding to a feature map generated based on a plurality of training samples, and wherein the embedding gradient weight is determined based on querying a feature space for the feature embedding associated with the subject data set; and generate signals for communicating interpretive data associated with the embedding gradient weight.

Abstract: Variational Autoencoders (VAEs) have been shown to be effective in modeling complex data distributions. Conventional VAEs operate with fully-observed data during training. However, learning a VAE model from partially-observed data is still a problem. A modified VAE framework is proposed that can learn from partially-observed data conditioned on the fully-observed mask. A model described in various embodiments is capable of learning a proper proposal distribution based on the missing data. The framework is evaluated for both high-dimensional multimodal data and low dimensional tabular data.