A handwriting recognition method which uses an invertible label embedding (encoding) algorithm to embed character strings into an Euclidean vector space as attribute vectors, uses a CNN to learn and predict attribute vectors of handwriting images in this Euclidean vector space, and then directly decodes a predicted attribute vector into a character string using a decoding algorithm that is the inverse of the invertible encoding algorithm. No lexicon is required to decode the predicted attribute vector. Thus, this method can recognize images containing handwritten digital sequences commonly encountered in many practical applications, such as quantities, dollar, date, phone number, social security numbers, zip code, etc. which are outside of common lexicons.

Methods and systems for topology-based clinical data mining are provided. An example system includes a pre-processing module to process the clinical datasets to generate a table of outcomes and a table of predictors of trial subjects. The system includes a graph construction module to generate metric graphs based on the table of outcomes. The metric graphs include nodes representing the subjects and edges selectively connecting the nodes according to pre-determined criteria. The graph construction module may select a graph of interest from the metric graphs and generate a compressed version of the graph of interest. The system may further include an interactive visualization module to display a graphical representation of the graph of interest or the compressed version, receive selection of groups of the trial subjects, automatically highlight groups of related subjects, and perform, using the table of predictors, a statistical analysis of predictors of subjects within the selected groups.

Before dimension reduction is performed while local data distribution is stored as neighborhood data, a distance between data to be subjected to the dimension reduction is calculated, and a parameter (a neighborhood number of the k-nearest neighbor algorithm or a size of a hypersphere) which determines the neighborhood data is determined for each data to be subjected to the dimension reduction. Thereafter, the dimension reduction is performed on the target data based on the determined parameter.

Various examples are directed to systems and methods for detecting training data for a generative model. A computer system may access generative model sample data and a first test sample. The computer system may determine whether a first generative model sample of the plurality of generative model samples is within a threshold distance of the first test sample and whether a second generative model sample of the plurality of generative model samples is within the threshold distance of the first test sample. The computer system may determine that a probability that the generative model was trained with the first test sample is greater than or equal to a threshold probability based at least in part on whether the first generative model sample is within the threshold distance of the first test sample, the determining also based at least in part on whether the second generative model sample is within the threshold distance of the first test sample.

An information processing apparatus includes an acquisition unit, a calculation unit, and a generation unit. The acquisition unit acquires information including information regarding multiple nodes and information regarding multiple links connecting the multiple nodes and acquires constraint information regarding node pairs included in the multiple nodes. The constraint information includes a positive constraint and a negative constraint. The calculation unit calculates, for each of multiple clusters, a classification proportion into which the multiple nodes are classified and calculates a degree of importance of each of the multiple clusters. The classification proportion represents a proportion in which each of the multiple nodes is classified as one of the multiple clusters. The generation unit generates a probability model for performing probabilistic clustering on the multiple nodes. The probability model is generated by using at least each of the information regarding the links, the constraint information, the classification proportion, and the degree of importance.

Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Embodiments described herein provide an apparatus comprising a processor to project voxels from a point cloud data set into an n-DoF space, and define successively less granular supervoxels at successively higher layer of abstraction in a view of the point cloud data set, and a memory communicatively coupled to the processor. Other embodiments may be described and claimed.

Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Described are methods, systems, and computer-program product embodiments for selecting a face image based on a name. In some embodiments, a method includes receiving the name. Based on the name, a name vector is selected from a plurality of name vectors in a dataset that maps a plurality of names to a plurality of corresponding name vectors in a vector space, where each name vector includes representations associated with a plurality of words associated with each name. A plurality of face vectors corresponding to a plurality of face images is received. A face vector is selected from the plurality of face vectors based on a plurality of similarity scores calculated for the plurality of corresponding face vectors, where for each name vector, a similarity score is calculated based on the name vector and each face vector. The face image is output based on the selected face vector.

In a kernel learning apparatus, a data preprocessing circuitry preprocesses and represents each data example as a collection of feature representations that need to be interpreted. An explicit feature mapping circuit designs a kernel function with an explicit feature map to embed the feature representations of data into a nonlinear feature space and to produce the explicit feature map for the designed kernel function to train a predictive model. A convex problem formulating circuitry formulates a non-convex problem for training the predictive model into a convex optimization problem based on the explicit feature map. An optimal solution solving circuitry solves the convex optimization problem to obtain a globally optimal solution for training an interpretable predictive model.