Abstract: Disclosed are implementations that include a method for detecting anomalous data, including converting a set of data values representative of a multi-dimensional item into a nodes-and-edges graph representation of the item, applying a graph convolution process to the graph representation to generate a transformed graph representation for the item comprising a resultant transformed configuration of the nodes and edges representing the item, and determining, based on the transformed configuration, a probability that the item is anomalous. Another example method includes receiving input data at a neural network circuit comprising a plurality of node layers, with each of the plurality of node layers comprising respective one or more nodes, with the neural network circuit further comprising adjustable weighted connections connecting at least some nodes in different layers of the plurality of node layers. The method further includes removing one or more of the weighted connections at one or more time instances.

Abstract: Disclosed are implementations that include a method for detecting anomalous data, including converting a set of data values representative of a multi-dimensional item into a nodes-and-edges graph representation of the item, applying a graph convolution process to the graph representation to generate a transformed graph representation for the item comprising a resultant transformed configuration of the nodes and edges representing the item, and determining, based on the transformed configuration, a probability that the item is anomalous. Another example method includes receiving input data at a neural network circuit comprising a plurality of node layers, with each of the plurality of node layers comprising respective one or more nodes, with the neural network circuit further comprising adjustable weighted connections connecting at least some nodes in different layers of the plurality of node layers. The method further includes removing one or more of the weighted connections at one or more time instances.

Abstract: Disclosed are implementations that include a method for detecting anomalous data, including converting a set of data values representative of a multi-dimensional item into a nodes-and-edges graph representation of the item, applying a graph convolution process to the graph representation to generate a transformed graph representation for the item comprising a resultant transformed configuration of the nodes and edges representing the item, and determining, based on the transformed configuration, a probability that the item is anomalous. Another example method includes receiving input data at a neural network circuit comprising a plurality of node layers, with each of the plurality of node layers comprising respective one or more nodes, with the neural network circuit further comprising adjustable weighted connections connecting at least some nodes in different layers of the plurality of node layers. The method further includes removing one or more of the weighted connections at one or more time instances.