In various examples, a set of object trajectories may be determined based at least in part on sensor data representative of a field of view of a sensor. The set of object trajectories may be applied to a long short-term memory (LSTM) network to train the LSTM network. An expected object trajectory for an object in the field of view of the sensor may be computed by the LSTM network based at least in part an observed object trajectory. By comparing the observed object trajectory to the expected object trajectory, a determination may be made that the observed object trajectory is indicative of an anomaly.

A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

A system, platform, program product, and/or method for generating new composite insight templates that includes: running a machine learning model on a data set to obtain for each of a plurality of entities a risk score and feature-based insights; generating a list of top “n” features input to the machine learning model that contributes to the risk score for each entity; grouping entities based upon similar features input to the machine learning model that contributes to the risk score for each entity; generating a decision tree for at least one of the group of entities; extracting, from the decision tree generated for the at least one of the group of entities, one or more feature-based insights; generating, by applying subject matter input, a new composite insight based upon the one or more feature-based insights; and adding the new composite insight to insight templates.

Example embodiments relate to hierarchical rule clustering. The examples disclosed herein access information about a set of rules, where information for an individual rule comprises information about a set of hypershapes associated with the individual rule. A respective hypervolume for each set of hypershapes associated with each individual rule may be calculated based on the accessed information. A first rule and a second rule may be combined as a new individual rule in the set of rules based on overlaps between the calculated hypervolumes.

Systems and methods for providing an unsupervised classification model by converting unsupervised data to supervised data. In one implementation, a processing device can receive an unlabeled dataset comprising one or more data records. The processing device can divide the unlabeled dataset into a plurality of groups. The processing device can then generate, for each group of the plurality of groups, a corresponding label. The processing device can generate a labeled dataset by assigning, to each group of the plurality of groups, the corresponding label. The processing device can then classify the labeled dataset using a classification model.

A method includes receiving input data associated with an application, the input data including at least one complex object and converting the at least one complex objects of the input data to a linearized set of features. The method further includes performing an explainability service on the application in view of the linearized set of features of the at least one complex object to generate an explanation array.

The present teaching relates to method, system, and programming for responding to an image related query. Information related to each of a plurality of images is received, wherein the information represents concepts co-existing in the image. Visual semantics for each of the plurality of images are created based on the information related thereto. Representations of scenes of the plurality of images are obtained via machine learning, based on the visual semantics of the plurality of images, wherein the representations capture concepts associated with the scenes.

During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.

A cluster visualization apparatus is disclosed. A cluster visualization apparatus according to the present disclosure includes a state detector configured to obtain state information of a cluster configured with a plurality of boxes, a display, and a controller configured to display a three-dimensional model image configured with a plurality of layers corresponding to a plurality of network layers and to display an image corresponding to each of the plurality of boxes over at least one layer of the plurality of layers, based on the state information.

Systems and methods for automated resolution of over-specification and under-specification in a knowledge graph are disclosed. In embodiments, a method includes: determining, by a computing device, that a size of an object cluster of a knowledge graph meets a threshold value indicating under-specification of a knowledge base of the knowledge graph; determining, by the computing device, sub-classes for objects of the knowledge graph; re-initializing, by the computing device, the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph; and generating, by the computing device, an output based on information determined from the refined knowledge graph.