Abstract: The present disclosure relates to the electronic document review field and, more particularly, to various apparatuses and methods of implementing batch-mode active learning for technology-assisted review (TAR) of documents (e.g., legal documents).

Abstract: Graphical interactive model selection is provided. A response variable vector for each value of a group variable and an explanatory variable vector are defined. A wavelet function is fit to the explanatory variable vector paired with the response variable vector defined for each value of the group variable. Each fit wavelet function defines coefficients for each value of the group variable. A curve is presented for each value of the group variable and is defined by the plurality of coefficients of an associated fit wavelet function. An indicator is received of a request to perform functional analysis using the coefficients for each value of the of the group variable based on a predefined factor variable. A model is trained using the coefficients for each value of the group variable and a factor variable value associated with each observation vector of each plurality of observation vectors as a model effect.

Abstract: A matrix-multiplying-vector operation method and a processing device for performing the same are provided. The matrix-multiplying-vector method includes distributing, by a main processing circuit, basic data blocks of the matrix and broadcasting the vector to a plurality of the basic processing circuits. That way, the basic processing circuits can perform inner-product operations between the basic data blocks and the broadcasted vector in parallel. The results are then provided back to main processing circuit for combining. The technical solutions proposed by the present disclosure provide short operation time and low energy consumption.

Abstract: A fully connected operation method and a processing device for performing the same are provided. The fully connected operation method designates distribution data and broadcast data. The distribution data is divided into basic data blocks and distributed to parallel processing units, and the broadcast data is broadcasted to the parallel processing units. Operations between the basic data blocks and the broadcasted data are carried out by the parallel processing units before the results are returned to a main unit for further processing. The technical solutions disclosed by the present disclosure provide short Operation time and low energy consumption.

Abstract: An apparatus includes processor(s) to: receive a request for a data catalog; in response to the request specifying a structural feature, analyze metadata of multiple data sets for an indication of including it, and to retrieve an indicated degree of certainty of detecting it for data sets including it; in response to the request specifying a contextual aspect, analyze context data of the multiple data sets for an indication of being subject to it, and to retrieve an indicated degree of certainty concerning it for data sets subject to it; selectively include each data set in the data catalog based on the request specifying a structural feature and/or a contextual aspect, and whether each data set meets what is specified; for each data set in the data catalog, generate a score indicative of the likelihood of meeting what is specified; and transmit the data catalog to the requesting device.

Abstract: A matrix-multiplying-matrix operation method and a processing device for performing the same are provided. The matrix-multiplying-matrix method includes distributing, by a main processing circuit, basic data blocks of one matrix and broadcasting the other matrix to a plurality of the basic processing circuits. That way, the basic processing circuits can perform inner-product operations between the basic data blocks and the broadcasted matrix in parallel. The results are then provided back to main processing circuit for combining. The technical solutions proposed by the present disclosure provide short operation time and low energy consumption.

Abstract: A method of managing memory usage of a stored training set for classification includes calculating one or both of a first similarity metric and a second similarity metric. The first similarity metric is associated with a new training sample and existing training samples of a same class as the new training sample. The second similarity metric is associated with the new training sample and existing training samples of a different class than the new training sample. The method also includes selectively storing the new training sample in memory based on the first similarity metric, and/or the second similarity metric.

Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

Abstract: Examples of techniques for anomaly detection with feedback are described. An instance includes a technique is receiving a plurality of unlabeled data points from an input stream; performing anomaly detection on a point of the unlabeled data points using an anomaly detection engine; pre-processing the unlabeled data point that was subjected to anomaly detection; classifying the pre-processed unlabeled data point; determining the anomaly detection was not proper based on a comparison of a result of the anomaly detection and a result of the classifying of the pre-processed unlabeled data point; and in response to determining the anomaly detection was not proper, providing feedback to the anomaly detection engine to change at least one emphasis used in anomaly detection.

Abstract: A method for calculating an output of a neural network, including the steps of generating a first neural network that includes discrete edge weights from a neural network that includes precise edge weights by stochastic rounding; of generating a second neural network that includes discrete edge weights from the neural network that includes precise edge weights by stochastic rounding; and of calculating an output by adding together the output of the first neural network and of the second neural network.

Abstract: A method for learning latent representations of individual users in a personalization system uses a graph-based machine learning framework. A graph representation is generated based on input data in which the individual users are each represented by a node. The nodes are associated with labels. Node vector representations are learned by combining label latent representations from a vertex and neighboring nodes so as to reconstruct the label latent representation of the vertex and updating the label latent representations of the neighboring nodes using gradients resulting from application of a reconstruction loss. A classifier/regressor is trained using the node vector representations and the node vector representations are mapped to personalizations. Actions associated with the personalizations are then initiated.

Abstract: In some embodiments, affective-state-based artificial intelligence may be facilitated. One or more growth or decay factors for a set of affective attributes of an artificial intelligence entity may be determined, and a set of affective values, which are associated with the set of affective attributes, may be continuously updated based on the growth or decay factors. An input may be obtained, and a response related to the input may be generated based on the continuously-updated set of affective values of the artificial intelligence entity. In some embodiments, the growth or decay factors may be updated based on the input and subsequent to the updating of the decay factors, the affective values may be updated based on the updated growth or decay factors.

Abstract: A computer-implemented method of recognizing dental information associated with a dental model of dentition includes training a deep neural network to map a plurality of training dental models representing at least a portion of each one of a plurality of patients' dentitions to a probability vector including probability of the at least a portion of the dentition belonging to each one of a set of multiple categories. The category of the at least a portion of the dentition represented by the training dental model corresponds to the highest probability in the probability vector. The method includes receiving a dental model representing at least a portion of a patient's dentition and recognizing dental information associated with the dental model by applying the trained deep neural network to determine a category of the at least a portion of the patient's dentition represented by the received dental model.

Abstract: Approaches for large-scale classification and text summarization. In one embodiment, for example, the approach for large-scale classification includes predicting relevant classes of a new unseen case based on a classification model that is learned from a given knowledge base comprising labeled training data items. In another embodiment, for example, the approach for text summarization includes repurposing explicit semantic analysis (ESA) techniques for computing a text summary of a given text document.

Abstract: A system and method of training an artificial neural network. The method comprises determining an activation value for each node in a set of nodes of the artificial neural network, the activation values being determined by applying training data to the artificial neural network, and scaling the determined activation values for each of a plurality of the nodes in a portion of the artificial neural network. Each scaled activation value is determined using a scaling factor associated with a corresponding one of the plurality of nodes. Each scaling factor is determined based on a rank of the corresponding node. The method further comprises updating weights associated with each of the plurality of nodes in the portion of the artificial neural network using the determined scaled activation values to train the neural network.

Abstract: A method, a system, and a system of systems, that couples one or more non-interpretable systems to one or more interpretable systems by using the output results of the non-interpretable systems as the training targets for the interpretable systems. The method, the system, and the system of systems provide for non-interpretable complex nonlinear interactions among inputs by augmenting the set of in-sample and out-sample inputs. The result of the coupling is one or more resulting interpretable systems that allow for the development of explanations, justifications, and rationalizations for systems heretofore non-explainable or non-interpretable. The method, system and system of systems solve transparency and bias problems for non-interpretable systems and provides a basis for ethical systems, such as ethical artificial intelligent (AI) systems.

Abstract: The present disclosure relates to the electronic document review field and, more particularly, to various apparatuses and methods of implementing batch-mode active learning for technology-assisted review (TAR) of documents (e.g., legal documents).

Abstract: A system to aid in design for manufacturing an object includes a processor and a memory configured to store instructions. The processor is configured to receive first data representing a design of the object to be manufactured and second data representing a machine-learning model. The processor is configured to execute the instructions to generate third data using the first data and the second data. The third data indicates at least one of a modification to the design of the object or process conditions for production of the object. The processor is configured to send the design of the object, the process conditions, or both, to a manufacturing tool to enable production of the object. The machine-learning model is representative of production data and based at least partially on one or more of: object features, process parameters, environmental factors, and quality data.

Abstract: A performance assessment device for evaluating a building management system (BMS). The device includes a communication interface. The communication interface is configured to communicate with a BMS network, the BMS network in communication with the BMS. The device further includes a processing circuit. The processing circuit is configured to receive data related to the BMS via the communication interface. The processing circuit is further configured to evaluate the data related to the BMS to generate a current assessment of the attributes of the BMS, and to compare the current assessment of the attributes of the BMS to a previously determined assessment of the attributes of the BMS.

Abstract: An embodiment of the invention provides a method for determining relationships between physical entities, where one or more of the physical entities is associated with static feature(s) and changeable feature(s). An entity analytics engine determines that a first physical entity and a second physical entity may be in a relationship with a third physical entity based on a first rule and a first set of observations. The first rule is applicable to one or more static features of the first physical entity, the second physical entity, and the third physical entity. The first rule provides that the first physical entity and the second physical entity may be in a relationship with the third physical entity when the third physical entity includes one or more static features that are within a threshold degree of similarity to static features of the first physical entity and the second physical entity.