Abstract: In various embodiments, a process for constrained decoding and ranking of language models for code generation includes receiving a natural language input specifying a desired computer task. The process includes using a machine learning trained converter to convert the natural language input to an output in a computer language, including by, based on a specified grammar for the computer language, limiting eligible options for a token to include in the output in the computer language. The process includes providing the output in the computer language for computer execution.

Abstract: In various embodiments, a process for constrained decoding and ranking of language models for code generation includes receiving a natural language input specifying a desired computer task. The process includes using a machine learning trained converter to convert the natural language input to an output in a computer language, including by, based on a specified grammar for the computer language, limiting eligible options for a token to include in the output in the computer language. The process includes providing the output in the computer language for computer execution.

Abstract: In an embodiment, a process for graph decomposition includes initializing nodes and edges of a data graph for analysis using a computer, and performing message passing between at least a portion of the nodes of the data graph to determine a corresponding measure of interest for each node of at least a portion of the data graph. The process further includes receiving an identification of one or more nodes of interest in the data graph, performing message passing between at least a portion of the nodes of the data graph using at least the determined measures of interest to identify a corresponding subgraph of interest for each of the one or more nodes of interest in the data graph, and performing an analysis action using the one or more identified subgraphs of interest.

Abstract: In various embodiments, a process for assessing transactional graphs based on generator-discriminator networks includes using a generator network to generate a first set of transaction graph samples that are of a generated type, wherein the generator network is trained to optimize a predetermined objective function. The process includes sampling, from a collected dataset, a second set of transaction graph samples that are of a non-generated type; and providing the first set of transaction graph samples and the second set of transaction graph samples to a discriminator network, wherein the discriminator network is trained to classify a provided transaction graph sample as the generated type or the non-generated type. The process includes discriminating, by the discriminator network, each of at least a portion of the first set of transaction graph samples and the second set of transaction graph samples as the generated type or the non-generated type.

Abstract: In an embodiment, a process for automated rules management system includes receiving a specification of past predicted results of evaluation rules and corresponding observed outcomes. The process includes determining one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, assessing the one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, and optimizing result activations or priorities of at least a portion of the evaluation rules based at least in part on the assessment of the one or more sets of alternative activations or priorities.

Abstract: A graph of nodes and edges is received. An identification of a starting node in the graph is received. Traversal walks on the graph from the starting node are automatically performed, wherein performing each of the traversal walks includes traversing to a randomly selected next node until any of one or more stopping criteria is met. One or more processors are used to determine one or more metrics based on the traversal walks. At least a portion of the one or more metrics is used to predict an illicit activity or entity.

Abstract: In an embodiment, a process for automated rules management system includes receiving a specification of past predicted results of evaluation rules and corresponding observed outcomes. The process includes determining one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, assessing the one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, and optimizing result activations or priorities of at least a portion of the evaluation rules based at least in part on the assessment of the one or more sets of alternative activations or priorities.

Abstract: In an embodiment, a process for graph decomposition includes initializing nodes and edges of a data graph for analysis using a computer, and performing message passing between at least a portion of the nodes of the data graph to determine a corresponding measure of interest for each node of at least a portion of the data graph. The process further includes receiving an identification of one or more nodes of interest in the data graph, performing message passing between at least a portion of the nodes of the data graph using at least the determined measures of interest to identify a corresponding subgraph of interest for each of the one or more nodes of interest in the data graph, and performing an analysis action using the one or more identified subgraphs of interest.