Mechanisms for identifying a pattern of computing resource activity of interest, in activity data characterizing activities of computer system elements, are provided. A temporal graph of the activity data is generated and a filter is applied to the temporal graph to generate one or more first vector representations, each characterizing nodes and edges within a moving window defined by the filter. The filter is applied to a pattern graph representing a pattern of entities and events indicative of the pattern of interest, to generate a second vector representation. The second vector representation is compared to the one or more first vector representations to identify one or more nearby vectors, and one or more corresponding subgraph instances are output to an intelligence console computing system as inexact matches of the temporal graph.

Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.

Methods and systems for machine learning inferencing based on directed acyclic graphs are presented. A request for a machine learning application is received from a tenant application. A tenant identifier that identifies one of the tenants is determined from the request. Based on the tenant identifier and a type of the machine learning application, configuration parameters and a graph structure are determined. The graph structure defines a flow of operations for the machine learning application. Nodes of the graph structure are executed based on the configuration parameters to obtain a scoring result. Execution of a node causes a machine learning model generated for the first tenant to be applied to data related to the request. The scoring result is returned in response to the request.

A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.

In one aspect, there is provided a method performed by one or more data processing apparatus, the method including receiving a training dataset having multiple training examples, where each training example includes: (i) an image, and (ii) a segmentation defining a target region of the image that has been classified as including pixels in a target category. The method further includes determining a respective refined segmentation for each training example, including, for each training example, processing the target region of the image defined by the segmentation for the training example using a de-noising neural network to generate a network output that defines the refined segmentation for the training example. The method further includes training a segmentation machine learning model on the training examples of the training dataset, including, for each training example training the segmentation machine learning model to process the image included in the training example to generate a model output that matches the refined segmentation for the training example.

Methods and systems for multi-model scoring in a multi-tenant system are presented. A request for a machine learning application is received from a tenant application. A tenant identifier that identifies one of the multiple tenants is determined. Based on the tenant identifier and a type of the machine learning application, a first and a second machine learning models are determined. The first machine learning model was generated based on a first training data set associated with the tenant identifier. The second machine learning model that was generated based on a second training data set associated with the tenant identifier. A flow of operations that includes running the first and second machine learning models with data related to the request is executed to obtain a scoring result. The scoring result is returned to the tenant application in response to the request.

Method and system for assisting electronic chip design, comprising: receiving netlist data for a proposed electronic chip design, the netlist data including a list of circuit elements and a list of interconnections between the circuit elements; converting the netlist data to a graph that represents at least some of the circuit elements as nodes and represents the interconnections between the circuit elements as edges; extracting network embeddings for the nodes based on a graph topology represented by the edges; extracting degree features for the nodes based on the graph topology; and computing, using a graph neural network, a congestion prediction for the circuit elements that are represented as nodes based on the extracted network embeddings and the extracted degree features.

Embodiments of the present disclosure relate to a method, a device and a computer-readable storage medium for data processing. The method for data processing comprises: obtaining a set of observation samples regarding a plurality of factors, one of the set of observation samples comprising respective observed values of the plurality of factors. The method further comprises: estimating, for each of the plurality of factors and based on the set of observation samples, a distribution that differences between observed values of the factor and estimated values of the factor follow. The method further comprises determining, based at least on the estimated distribution, a causal structure representing a causal relationship among the plurality of factors. Embodiments of the present disclosure further provide a device and a computer-readable storage medium for implementing the above method. The embodiments of the present disclosure can accurately and robustly discover the causal relationship among a plurality of factors without making any assumptions about the relationship between the data distribution and the factors, and affect the observed value of the target factor based on the causal relationship.

Method and system for predicting labels for nodes in an observed graph, including deriving a plurality of random graph realizations of the observed graph; learning a predictive function using the random graph realizations; predicting label probabilities for nodes of the random graph realizations using the learned predictive function; and averaging the predicted label probabilities to predict labels for the nodes of the observed graph.

A navigation system for a vehicle comprises onboard sensors including a vision sensor, and an onboard map database of terrain maps. An onboard processer, coupled to the sensors and map database, includes a position PDF filter, which performs a method comprising: receiving image data from the vision sensor corresponding to terrain images captured by the vision sensor of a given area; receiving map data from the map database corresponding to a terrain map of the area; generating a first PDF of image features in the image data; generating a second PDF of map features in the map data; generating a measurement vector PDF by a convolution of the first PDF and second PDF; estimating a position vector PDF using a non-linear filter that receives the measurement vector PDF; and generating statistics from the estimated position vector PDF that include real-time measurement errors of position and angular orientation of the vehicle.