Abstract: A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Abstract: A neural network computation apparatus includes a first processing block including a plurality of processing units that each perform a matrix multiplication operation on input data and weights, and a second processing block including a plurality of element-wise operation processing groups. The element-wise operation processing group selectively perform a first neural network computation operation and a second neural network computation operation. The first neural network computation operation comprises the matrix multiplication operation on the input data and the weights and an activation operation on a result value of the matrix multiplication operation, and the second neural network computation operation comprises an activation operation on the result value of the matrix multiplication operation, which is transferred from the first processing block, and an element-wise operation.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology into an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component parameter values for the analog components.

Abstract: An example system includes a processor to receive a data set. The processor can generate a data slice rule based on a data observation for a data point in the data set. The processor can generate an instance of data based on the generated data slice rule.

Abstract: A general matrix-matrix (GEMM) accelerator core includes first and second buffers, a control logic circuit, and a first processing element (PE). The first buffer receives a elements of a first matrix A of activation values. The second buffer receives b elements of a second matrix B of weight values. The control logic circuit replaces a zero-valued a element in a first column of the first buffer with a nonzero-valued a element that is within a maximum borrowing distance of a location of the zero-valued a element in the first column of the first buffer. The PE receives a elements from the first column of the first buffer including the nonzero-valued element a selected to replace the zero-valued a element and receives b elements from locations in the second buffer that correspond to locations in the first buffer from where the a elements have been received by the PE.

Abstract: A feature engineering application receives a plurality of data sets from different data sources for training a model for making a prediction based on new data. The feature engineering application generates primitives based on the data sets. A primitive is to be applied to a variable in the data sets to synthesize a feature. The feature engineering application also receives a temporal parameter that specifies a temporal value for generating time-based features. After the primitives are generated and the temporal parameter is received, the feature engineering application aggregates the plurality of data entities based on primary variables in the plurality of data entities and generate an entity set based on the aggregation. The feature engineering application then synthesize features, including the time-based features, based on the entity set, at least some of the primitives, and the temporal parameter.

Abstract: A method includes obtaining, using at least one processor of an electronic device, one or more instance level supervised artificial intelligence (AI) models. The method also includes obtaining, using the at least one processor, aggregated level label information related to the one or more instance level supervised AI models. The method further includes obtaining, using the at least one processor, instance level feature information related to the one or more instance level supervised AI models. In addition, the method includes training, using the at least one processor, the one or more instance level supervised AI models using the instance level feature information and the aggregated level label information to obtain one or more trained instance level supervised AI models.

Abstract: The instant system and methods solves the cold start problem through various systems and methods directed to aggregating user interaction data associated with a user over a period of time, generating an embedding model based on the aggregated user interaction data, generating a content embedding vector based on the embedding model, generating an embedding profile vector based on the embedding model, storing the embedding profile vector in a storage device, receiving each of the content embedding vector and embedding vector profile for training a ranking model, and generating a predicted list of one or more content items of interest for recommending to the user.

Abstract: A framework is presented that provides a shift in the conceptual and practical realization of privacy-preserving interference on deep neural networks. The framework leverages the concept of the binary neural networks (BNNs) in conjunction with the garbled circuits protocol. In BNNs, the weights and activations are restricted to binary (e.g., ±1) values, substituting the costly multiplications with simple XNOR operations during the inference phase. The XNOR operation is known to be free in the GC protocol; therefore, performing oblivious inference on BNNs using GC results in the removal of costly multiplications. The approach consistent with implementations of the current subject matter provides for oblivious inference on the standard DL benchmarks being performed with minimal, if any, decrease in the prediction accuracy.

Abstract: A system and method for adjusting input data of a decision-making neural network is provided, wherein the system includes a data-dividing neural network apparatus and a data processing apparatus. The data-dividing neural network apparatus receives an input data and divides the input data into a plurality of sub data including a first sub data and a second sub data. The data processing apparatus is coupled to the data-dividing neural network apparatus to receive the sub data, and process the first sub data and the second sub data by different ways when the sub data is processed, so that the first sub data and the second sub data are differently adjusted. The decision-making neural network is electrically coupled to the data processing apparatus to take the processed sub data as input data. As a result, the neural network can change the final output results.

Abstract: According to example embodiments of the present disclosure, a method, device and computer program product for data simulation are proposed. The method for data simulation includes: obtaining first data pattern information that is associated with a first set of operations executed on real data in a data protection system; generating, based on the first data pattern information, second data pattern information that is associated with a second set of operations executable by the data protection system; and generating, based on the second data pattern information, simulation data different from the real data, for the data protection system to execute the second set of operations on the simulation data. Thereby, the present solution can simulate efficiently and reliably a data pattern of real data, and thus generating simulation data of a data pattern similar to that of the real data.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: obtaining a set of items that have been grouped together as matching items in a group; generating, using an ensemble learning model, a predictive indication of a mismatched item grouped together in error as part of the set of items, wherein the ensemble learning model comprises at least two detection models that are performed simultaneously with each other to output predictive indications comprising the predictive indication; and determining a final mismatch decision for an item of the set of items, wherein the final mismatch decision is based on the predictive indication, and wherein the item comprises the mismatched item. Other embodiments are disclosed.

Abstract: A system includes an interface and a processor. The interface is configured to receive a predicted output signal. The processor is configured to: a) determine whether the predicted output signal satisfies a constraint set; and b) in response to the predicted output signal not satisfying the constraint set, determine a transformed output signal that satisfies the constraint set by: 1) determining a set of transformed predicted output signals that satisfy the constraint set, wherein a transformed predicted output signal of the set of transformed predicted output signals that satisfy the constraint set comprises the predicted output signal modified by one or more value modifications; 2) selecting a transformed predicted output signal of the set of transformed predicted output signals that satisfy the constraint set; and 3) providing the transformed predicted output signal of the set of transformed predicted output signals that satisfy the constraint set.

Abstract: In various embodiments, a character engine models a character that interacts with users The modeling techniques include evaluating user input data that is associated with a user device to identify a user intent and an assessment domain, selecting a first set of inference algorithms from a plurality of inference algorithms based, at least in part, on the user intent and the assessment domain, and applying the user intent and the assessment domain to the first set of inference algorithms to generate a plurality of inferences.

Abstract: A system for use with an artificial intelligence (AI) model configured to accept text input, such as generative pre-trained transformer (GPT), that detects and tags trusted instructions and nontrusted instructions of an input provided by a user responsive to an AI model prompt. The system uses reinforcement learning (RL) and a set of rules to remove the untrusted instructions from the input and provide only trusted instructions to the AI model. The input is represented as tokens, wherein the trusted instructions and the untrusted instructions are represented using incompatible token sets.

Abstract: A method and system for implementing superseded federated learning. Superseded federated learning may entail a novel, performance-efficient federated learning technique designed to further decouple multiparty dependency on one another, as well as any third-parties, while collaborating in multiparty computations. Specifically, unlike any current federated learning methodology, superseded federated learning eliminates the complex and often inefficient coordination amongst parties, as well as removes third-party participation, during the classification or prediction inference phase of multiparty collaborations.

Abstract: The present disclosure relates generally to an integrated machine learning platform. The machine learning platform can convert machine learning models with different schemas into machine learning models that share a common schema, organize the machine learning models into model groups based on certain criteria, and perform pre-deployment evaluation of the machine learning models. The machine learning models in a model group can be evaluated or used individually or as a group. The machine learning platform can be used to deploy a model group and a selector in a production environment, and the selector may learn to dynamically select the model(s) from the model group in the production environment in different contexts or for different input data, based on a score determined using certain scoring metrics, such as certain business goals.

Abstract: A software package is received and unpacked into a plurality of components. Features are extracted from each component which are indicative (i.e., useful, etc.) in determining whether such component presents a software supply chain risk. The extracted features are consumed by one or more machine learning models to determine a level of supply chain risk associated with the component. This determined level of supply chain risk can be provided to a consuming application or process. Component identities can also be identified using machine learning or other similarity analyses. In some cases, embeddings are used to characterize risk and/or provide component identities. Related apparatus, systems, techniques and articles are also described.

Abstract: A software package is received and unpacked into a plurality of components. Features are extracted from each component which are indicative (i.e., useful, etc.) in determining whether such component presents a software supply chain risk. The extracted features are consumed by one or more machine learning models to determine a level of supply chain risk associated with the component. This determined level of supply chain risk can be provided to a consuming application or process. Component identities can also be identified using machine learning or other similarity analyses. In some cases, embeddings are used to characterize risk and/or provide component identities. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes determining that conditions of a processing chamber have changed since a trained machine learning model associated with the processing chamber was trained. The method further includes determining whether a change in the conditions of the processing chamber is a gradual change or a sudden change. Responsive to determining that the change in the conditions of the processing chamber is a gradual change, the method further includes performing a first training process to generate a new machine learning model. Responsive to determining that the change in the conditions of the processing chamber is a sudden change, the method further includes performing a second training process to generate the new machine learning model. The first training process is different from the second training process.