Abstract: A system that provides a mathematical formulation for new problem of model validation and model selection in presence of test data feedback. The system comprises a memory that stores computer-executable components. A processor, operably coupled to the memory, executes the computer-executable components stored in the memory. A selection component selects a metric of performance evaluation accuracy; and a configuration component configures performance evaluation schemes for machine learning algorithms. A characterization component employs a supervised learning-based approach to characterize relationship between the configuration of the performance evaluation scheme and fidelity of performance estimates; and an optimization component that optimizes accuracy of the machine learning algorithms as a function of size of training data set relative to size of validation data set through selection of values associated with the configuration parameters.

Abstract: A system analyzes periodically collected data associated with entities, for example, users, servers, or systems. The system determines anomalies associated with populations of entities. The system excludes anomalies from consideration to increase efficiency of execution. The system may rank the anomalies based on relevance scores. The system determines relevance scores based on various factors describing the sets of entities. The system may present information describing the anomalies based on the ranking. The system may use a machine learning based model for predicting likelihoods of outcomes associated with sets of entities. The system generates alerts for reporting the outcomes based on the predictions.

Abstract: A computation method used in a convolutional neural network is provided. The method includes: receiving original data; determining a first optimal quantization step size according to a distribution of the original data; performing fixed-point processing to the original data according to the first optimal quantization step size to generate first data; inputting the first data to a first layer of the convolutional neural network to generate first output data; determining a second optimal quantization step size according to a distribution of the first output data; performing the fixed-point processing to the first output data according to the second optimal quantization step size to generate second data; and inputting the second data to a second layer of the convolutional neural network.

Abstract: The present disclosure provides computing systems and techniques for validating a decision model against a cannon of regulation. A server can deconstruct a decision model into a number of branching decisions and also generate a Markov chain comprising a number of sequences from a cannon of regulation. The server can compare the branching decisions to the sequences and can validate the decision model with the cannon of regulation based on the comparison.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a hierarchical topic machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the hierarchical topic machine learning operation; and, generating a cognitive insight based upon the cognitive profile generated using the hierarchical topic machine learning operation.

Abstract: Techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters are described. An exemplary method includes receiving a request to determine a search space for at least one hyperparameter of a machine learning algorithm; determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets; and tuning the machine learning algorithm using the determined optimal hyperparameter values for the at least one hyperparameter of the machine learning algorithm to generate a machine learning model.

Abstract: According to an embodiment, a reinforcement learning system includes a memristor array in which each of a plurality of first direction lines corresponds to one of a plurality of states, and each of a plurality of second direction lines corresponds to one of a plurality of actions, a first voltage application unit that individually applies voltage to the first direction lines, a second voltage application unit that individually applies voltage to the second direction lines, a action decision circuit that decides action to be selected by an agent in a state corresponding to a first direction line to which a readout voltage is applied, a action storage unit that stores action selected by the agent in each state that can be caused in an environment, and a trace storage unit that stores a time at which the state is caused by action selected by the agent.

Abstract: A method in which user information is transmitted from at least one data source to a processing unit of a learning device. The user information is used, by the processing unit via a machine learner, to generate at least one user model. The at least one user model is adapted via an adaptation of parameters used by the at least one machine learner to generating the at least one user model. The parameters, used by the at least one machine learner for generating the at least one user model, are adapted as a function of at least one predefined rule. The user model generated on the basis of the adapted parameters is used to personalize at least one terminal.

Abstract: A method of determining whether a user has fallen comprises detecting a potential fall using a motion sensing device, updating a probability of the potential fall being an actual fall based on an additional sensor, and updating the probability of the potential fall being an actual fall based on user context, the user context including an identified activity prior to the potential fall.

Abstract: Systems and methods for determining relative importance of one or more variables in a non-parametric model include: receiving, raw values of the variables corresponding to one or more entities; processing the raw values using a statistical model to obtain probability values for the variables and an overall prediction value for each entity; determining a plurality of cumulative distributions for the variables based on the raw values and the number of entities having a specific raw value; grouping the variables into a plurality of equally sized buckets based on the cumulative distributions; determining a mean probability value for each bucket; assigning a rank number for each bucket based on the mean probability values; compiling a table for the entities based on the raw values and the buckets corresponding to the raw values; and determining the relative importance of the variables for the entities based on the rank numbers.

Abstract: A method, computer system, and a computer program product for assessing a likelihood of success associated with developing at least one machine learning (ML) solution is provided. The present invention may include generating a set of questions based on a set of raw training data. The present invention may also include computing a feasibility score based on an answer corresponding with each question from the generated set of questions. The present invention may then include, in response to determining that the computed feasibility score satisfies a threshold, computing a level of effort associated with developing the at least one ML solution to address a problem. The present invention may further include presenting, to a user, a plurality of results associated with assessing the likelihood of success of the at least one ML solution.

Abstract: A question and answer (QA) system, computer program product, and computer-implemented method configured to determine an answer to a question that includes a measurement value. In one example, the QA system receives a question and analyzes the question to identify a measurement value specified in the question. The QA system determines relevant passages to the question. The QA system assigns a measurement value confidence score to a relevant passage based on a comparison of the measurement value specified in the question and a second measurement value specified in the relevant passage. The QA system determines an order of the relevant passages using the measurement value confidence score of each of the relevant passages. The QA system determines an answer to the question based on the order of the relevant passages.

Abstract: Methods, systems and computer program products implementing character-level deep neural networks for information extraction are disclosed. A system uses character-level information retrieved from a transaction record to classify the transaction as a whole and to tag individual sections of the transaction record by entity type. The system processes the transaction record using multiple and separate character-level models. The system can use a one-dimensional neural network for featurization fed into a fully connected network for classification for identifying the most common classes of a transaction record. The system can identify one or more entities, e.g., service provider names, from the transaction using an RNN. The RNN can include one or more LSTM models. The LSTM models can be BI-LSTM models.

Abstract: A learning device includes: a learning unit configured to read out feature amounts of learning data from a data memory and derive a branch condition for a node of a decision tree based on the feature amounts, to perform learning of the decision tree; and a discriminator configured to perform determining, in accordance with the branch condition, a node to which learning data is to be branched from the node corresponding to the branch condition. The learning unit is configured to, in parallel with processing of the discriminator reading out learning data at a specific node from the data memory via a first port of the data memory and performing the determining, read out, from the data memory via a second port, learning data at a node on which the discriminator is configured to perform determining subsequent to the specific node and derive the branch.

Abstract: Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.

Abstract: The subject disclosure relates to employing a computer-implemented method that sources, by a system operatively coupled to a processor, a set of personalized data comprising at least one of biometric data, statistical data, or contextual data. The method also includes determining, by the system, predictive relationships based on an evaluation of the set of personalized data. In another aspect, the method includes generating, by the system, a personal dynamic decision grid comprising a set of decision data coupled to a set of scores based on the predictive relationships, wherein the set of scores represent a probability of performing respective decisions of the set of decisions.

Abstract: An approach for continuously provisioning machine learning models, executed by one or more computer nodes to provide a future prediction in response to a request from one or more client devices, is provided. The approach generates, by the one or more computer nodes, a machine learning model. The approach determines, by the one or more computer nodes, whether the machine learning model is a new model. In response to determining the machine learning model is not the new model, the approach retrieves, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model. The approach determines, by the one or more computer nodes, a difference between the associated model and the new persistent model. Further, in response to determining the machine learning model is the new model, the approach generates, by the one or more computer nodes, one or more model containers.

Abstract: An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Abstract: A mechanism is described for facilitating learning and application of neural network topologies in machine learning at autonomous machines. A method of embodiments, as described herein, includes monitoring and detecting structure learning of neural networks relating to machine learning operations at a computing device having a processor, and generating a recursive generative model based on one or more topologies of one or more of the neural networks. The method may further include converting the generative model into a discriminative model.

Abstract: A method of determining a second alimentary provider is disclosed. The method inputs an order for an alimentary combination from a user. The alimentary combination is prepared by a first alimentary provider. The method classifies a plurality of alimentary providers. The method computes a alimentary provider score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a first machine-learning process, the machine learning process trained by training data correlating alimentary provider scores to alimentary combinations. The method selects a second alimentary provider from the plurality of alimentary providers as a function of the alimentary provider score. The method outputs the second alimentary provider to the user. A system of determining a second alimentary provider is also disclosed.