Abstract: A standardized data model (“SDM”) includes standardized data types that indicate classifications of data elements. In a data service platform, such as a marketing data platform, a data standardization module classifies received data elements. One or more components included in the data standardization module are trained using supervised or unsupervised learning techniques to classify received data elements into a standardized data type included in the SDM. In some cases, an output of an unsupervised learning phase is provided as an input to a supervised learning phase. In some cases, a classified data element is modified by the data standardization module to indicate the standardized data type into which the data element is classified.

Abstract: A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user's input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user's selection actions thereof. The method includes analyzing the selected items to learn the user's content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user's learned content preferences determined to be relevant to the items of the first content system.

Abstract: The probabilistic accumulation method is an independent and comprehensive approach for validating third-party catastrophe models. The method starts with a standard technique, namely, a limited scenario accumulation analysis, and extends the approach via a novel sampling methodology to evaluate the third-party models against a significantly larger data set. The sampling approach includes a technique for extending the fixed frequency and severity assumption used by the vendor catastrophe models. The result is a more complete set of loss estimates against which to evaluate the vendor model output.

Abstract: A computing system classifies distributed data. A first computation request is sent to worker computing devices. A first response is received from each worker computing device. Each first response includes a first matrix computed as a second order derivative of a logarithm of a predefined likelihood function on a subset of training data distributed to each respective worker computing device. A global first matrix is defined by concatenating the first matrix from each worker computing device. A kernel matrix is computed using the training data and a predefined kernel function. A second computation request is sent to the worker computing devices. The second computation request indicates that each worker computing device compute a classification probability for each observation vector distributed to a respective worker computing device using the defined global first matrix and the computed kernel matrix. The determined classification probability is output for each observation vector.

Abstract: An operation method of a neural network, a training method, and a signal processing apparatus are provided. The operation method includes receiving an output signal from a first neural network, and converting a first feature included in the output signal to a second feature configured to be input to a second neural network, based on a conversion rule controlling conversion between a feature to be output from the first neural network and a feature to be input to the second neural network. The operation method further includes generating an input signal to be input to the second neural network, based on the second feature, and transmitting the input signal to the second neural network.

Abstract: The present disclosure provides a method and equipment for searching a non-unique solution of a petrophysical property combination in history matching.

Abstract: Artificial intelligence is introduced into an electronic meeting context to perform various tasks before, during, and/or after electronic meetings. The tasks may include a wide variety of tasks, such as agenda creation, participant selection, real-time meeting management, meeting content supplementation, and post-meeting processing. The artificial intelligence may analyze a wide variety of data such as data pertaining to other electronic meetings, data pertaining to organizations and users, and other general information pertaining to any topic. Capability is also provided to create, manage, and enforce meeting rules templates that specify requirements and constraints for various aspects of electronic meetings.

Abstract: Methods and systems are provided for providing recommendations from a recommendation system for an analytics system. A recommendation system can be trained using user intent and context. Such user intent can be determined using a user history of interaction with an analytics system. The user history can either be that of the user accessing the recommendation system or an exemplary user history to broaden the recommendations made by the recommendation system. Such context can be determined using context features within the analytics system. The trained recommendation system generated using user intent and context can provide analytics recommendations based on a current context of a user that predict the intent of the user.

Abstract: Techniques for decoupling cognitive model training from execution of the cognitive model are provided. In one example, a computer program product is provided that determines cognitive data based on context data and a model of interpreting the context data. The cognitive data can comprise prediction data that represents a prediction relating to a state of an environment. The context data and the cognitive data can be transmitted to a server, and an updated model can be received in response.

Abstract: A method for parallelizing a training of a model using a matrix-factorization-based collaborative filtering algorithm may be provided. The model can be used in a recommender system for a plurality of users and a plurality of items. The method includes providing a sparse training data matrix, selecting a number of user-item co-clusters, and building a user model data matrix by matrix factorization such that a computational load for executing the determining updated elements of the factorized sparse training data matrix is evenly distributed across the heterogeneous computing resources.

Abstract: A machine learning device that performs machine learning with respect to a changing unit configured to change a parameter of a control unit configured to control a servo motor and a compensation value of at least one of a position command and a torque command includes: a state information acquisition unit configured to acquire state information including the position command, a positional error, a combination of the parameter and the compensation value; an action information output unit configured to output action information including adjustment information of the combination of the parameter and the compensation value included in the state information; a reward output unit configured to output a reward value of reinforcement learning based on the positional error included in the state information; and a value function updating unit configured to update a value function based on the value of the reward, the state information, and the action information.

Abstract: A technology to build emulated nervous systems is presented here, as well as the interface method for operating the emulated nervous system. The technology provides for inclusion of neuroanatomically accurate definitions organized hierarchically. This permits a highly realistic nervous system to be created and interact with its surrounding environment.

Abstract: Techniques for improving products based on data-driven models are provided. In one example, a system comprises a receiving component that receives product data representing information about a set of products, wherein a first product of the set of products comprises a first combination of a first set of ingredients, and wherein the product data comprises product composition data representing a composition of the first product. The system further comprises a learning component that generates product space data representing a product space that characterizes the set of products and respective degrees of similarity between members of the set of products, wherein a degree of similarity between the first product and a second product of the set of products is determined based on product distance data representing a determined distance metric resulting from a comparison of the first set of ingredients to a second set of ingredients combined to produce the second product.

Abstract: Resource-action pairs are tracked for a user during network sessions with a network service. A predicted path for a future network session is learned. When the user logs into the network service for a subsequent network session with the network service, the predicted path can be executed automatically for the user with the network service during that subsequent network session.

Abstract: Technology for generating a consistently labeled training dataset. For each one of multiple previously labeled texts, a distance between the previously labeled text and a current text to be labeled is generated by comparing a list of tokens for the previously labeled text to a list of tokens for the current text to determine an overlap value equal to a number of tokens that match between the list of tokens for the previously labeled text and the list of tokens for the current text, and using the overlap value to calculate a distance between the previously labeled text and the current text that is inversely correlated to the overlap value. Previously labeled texts that are most similar to the current text are identified as those previously labeled texts having the shortest distances to the current text, and are displayed with their previously assigned labels in a label selection user interface.

Abstract: Certain embodiments involve learning user preferences and predicting user behavior based on sequential user behavior data. For example, a system obtains data about a sequence of prior actions taken by multiple users. The system determines a similarity between a prior action taken by the various users and groups the various users into groups or clusters based at least in part on the similarity. The system trains a machine-learning algorithm such that the machine-learning algorithm can be used to predict a subsequent action of a user among the various users based on the various clusters. The system further obtains data about a current action of a new user and determines which of the clusters to associate with the new user based on the new user's current action. The system determines an action to be recommended to the new user based on the cluster associated with the new user. The action can include a series or sequence of actions to be taken by the new user.

Abstract: Various implementations for assigning rules and creating rules using templates are described herein. In one example implementation, a model is determined, one or more components of the model are determined, a rule from a set of one or more predefined rules is determined, and the rule is assigned to the model. The rule has one or more parameters matching the one or more components of the model.

Abstract: Embodiments of the present disclosure discloses method and system for determining classification of text. The present disclosure discloses to receive text from plurality of texts and generating a pair of vector representation of the text using trained model parameters of a pair of LSTM units. The trained model parameters are obtained based on training of classification system using plurality of similar pair of texts and plurality of dissimilar pair of texts from the plurality of texts. Further, pair of vector representations are combined using a combiner operator to obtain a combined vector representation. The combiner operator is selected from a plurality of combiner operators based on the training using accuracy of classifier of classification system. The combined vector representation is provided to the classifier for determining classification of text. The present disclosure enhances the performance and generalisation of a classifier in cases of a multi-class classification.

Abstract: Techniques are provided for predicting DNA accessibility. DNase-seq data files and RNA-seq data files for a plurality of cell types are paired by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype. A neural network is configured to be trained using batches of the paired data files, where configuring the neural network comprises configuring convolutional layers to process a first input comprising DNA sequence data from a paired data file to generate a convolved output, and fully connected layers following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the paired data file and process the concatenation to generate a DNA accessibility prediction output. The trained neural network is used to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type.

Abstract: Methods and systems of training a neural network includes training a neural network based on training data. Weights of a layer of the neural network are multiplied by an attrition factor. A block of weights is pruned from the layer if the block of weights in the layer has a contribution to an output of the layer that is below a threshold.