A method and system for clustering-based panoptic segmentation of point clouds and a method of training the same are provided. Features of a point cloud that includes a plurality of points are extracted. Clusters of the plurality of points corresponding to objects from the features of the point cloud frame are identified. A subset of the plurality of points is selectively shifted using the features and the clusters of the plurality of points via a neural network that is trained to recognize a subset of points of objects that are closer to points of other objects than a distance between centroids of the corresponding objects and shift the subset of points away from the other objects.

This document generally relates to techniques for testing or training data augmentation. One example includes a method or technique that can include accessing a repository of private data items. The repository can provide a distribution of the private data items that is representative of a designated real-world scenario for a machine learning model. The method or technique can also include assigning classifications to the private data items in the repository. The method or technique can also include augmenting a testing or training set for the machine learning model based at least on the classifications of the private data items to obtain an augmented testing or training set that is relatively more representative of the distribution of classifications in the repository.

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Various examples related to real time detection with recurrent networks are presented. These can be utilized in automatic insect recognition to provide accurate and rapid in situ identification. In one example, among others, a method includes training parameters of a kernel adaptive autoregressive-moving average (KAARMA) using a signal of an input space. The signal can include source information in its time varying structure. A surrogate embodiment of the trained KAARMA can be determined based upon clustering or digitizing of the input space, binarization of the trained KAARMA state and a transition table using the outputs of the trained KAARMA for each input in the training set. A recurrent network detector can then be implemented in processing circuitry (e.g., flip-flops, FPGA, ASIC, or dedicated VLSI) based upon the surrogate embodiment of the KAARMA The recurrent network detector can be configured to identify a signal class.

Techniques to generate a flavor profile using artificial intelligence are disclosed. A flavor classifier classifies flavors for a given set of ingredients of a recipe from a set of possible classes of flavors. The flavor classifier may use different deep learning models to allow for different granularity levels corresponding to each flavor based on desired preciseness with classification of a particular flavor. A respective flavor predictor may or may not be used for each granularity level based on output of a certainty level classifier used for determining a preceding level of granularity.

Techniques described herein relate to a method for managing data nodes of data node clusters. The method includes obtaining, by a data node manager, a request to deploy a model to a data node; in response to obtaining the model deployment request: identifying, by the data node manager, a data node cluster associated with the data node; making a first determination, by the data node manager, that the data node cluster is associated with an available distilled dataset; and in response to the first determination: generating, by the data node manager, a model using the available distilled dataset; and deploying, by the data node manager, the model to the data node.

In some implementations, a device may receive, from a data stream, a set of data points arranged in a dimensional data space. The device may compare the set of data points to identify one or more clusters using values of a distance parameter for data points included in the set of data points, wherein the values of distance parameter includes different values of the distance parameter for different data points. The device may transmit an indication of the one or more clusters to cause a device to display information associated with the one or more clusters. The device may receive, from the device, feedback information associated with at least one data point, wherein the feedback information indicates that at least one data point is associated with an error. The device may modify a value of the distance parameter associated with the at least one data point to a modified value.

Systems, methods, and non-transitory computer-readable media can be configured to determine a video embedding for a video content item based at least in part on a first machine learning model. A set of music embeddings can be determined for a set of music content items based at least in part on a second machine learning model. The set of music content items can be ranked based at least in part on the video embedding and the set of music embeddings.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for recommending a protection strategy. The method includes obtaining contents of attributes of a plurality of data assets adjusted. The method further includes generating a plurality of vector representations for the plurality of data assets based on the contents of the attributes. The method further includes dividing the plurality of data assets into at least one category based on the plurality of vector representations. The method further includes if it is determined that a protection strategy for one data asset in the at least one category exists, determining the protection strategy as a recommended strategy for another data asset in the at least one category. By means of the method, a user can easily select a proper protection strategy and can effectively reuse existing strategies, thereby improving user experience.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for recommending a protection strategy. The method includes obtaining contents of attributes of a plurality of data assets adjusted. The method further includes generating a plurality of vector representations for the plurality of data assets based on the contents of the attributes. The method further includes dividing the plurality of data assets into at least one category based on the plurality of vector representations. The method further includes if it is determined that a protection strategy for one data asset in the at least one category exists, determining the protection strategy as a recommended strategy for another data asset in the at least one category. By means of the method, a user can easily select a proper protection strategy and can effectively reuse existing strategies, thereby improving user experience.