Abstract: A residential home characteristics inferring method and system that receives information about energy usage by an energy user, determines using a processor and the received information about energy usage average daily usage during a heating season and average daily usage during a shoulder season, and identifies the fuel type used for heating by the energy user using the determined average daily usage during the heating season and the determined average daily usage during the shoulder season.

Abstract: A computer-implemented content suggestion engine provides content suggestions to a requesting user based on information about content items that other users may have independently categorized or organized into folders within a content repository. Embodiments of the method comprise a content repository having a plurality of content items, where each content item is associated with one or more user-created folders. Embodiments further comprise receiving, via a network, a suggestion request for suggested content, where the suggestion request identifies a first content item for which suggestions are sought. Other content items in the content repository are then identified as potential suggestions based on the application of a formal relationship between the first content item and the potential suggested content items. One or more of the potential suggested content items may then be provided in response to the suggestion request via the network.

Abstract: A technology is described for predicting a launch time for a computing instance. An example method may include receiving a request for a predicted launch time to launch a computing instance on a physical host within a computing service environment. Data associated with launch features of a computing instance may then be obtained, where the launch features may be determined to have an impact on a launch time of the computing instance on a physical host within a computing service environment. The launch features of the computing instance may then be input to a machine learning model that outputs the predicted launch time for launching the computing instance within the computing service environment.

Abstract: A user management method according to the present disclosure includes: storing appliance use information including: appliance identification information for identifying an appliance; user information for identifying a user of the appliance; and an operating state of the appliance when the appliance was used; analyzing the appliance use information stored in the storing to identify, from among a plurality of functions of the appliance, one or more first functions each having a use frequency less than or equal to a threshold value; and providing the user with a notice which prompts use of the one or more first functions identified in the analyzing.

Abstract: A system and method for training a system for monitoring administration of medication. The method includes the steps of a method for training a medication administration monitoring apparatus, comprising the steps of defining one or more predetermined medications and then acquiring information from one or more data sources of a user administering medication. A first network is trained to recognize a first step of a medication administration sequence, and then a second network is trained to recognize a second step of a medication administration sequence based upon the training of the first network.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: In some aspects, a method may include initializing a first array and a second array with a random voltage value, passing a forward pass by pulsing an input voltage value from an input of the first array and an input of the second array, and reading output voltage values at an output of the first array and an output of the second array. The method may further include passing a backward pass into the inputs of both of the first and second arrays, and reading voltage values at the inputs of the first and second arrays. The method may further include updating, with the first array, a first matrix update on the first array, updating, with the second array, a first matrix update on the second, and updating, with the second array, a second matrix update on the second array.

Abstract: In some aspects, a method may include initializing a first array and a second array with a random voltage value, passing a forward pass by pulsing an input voltage value from an input of the first array and an input of the second array, and reading output voltage values at an output of the first array and an output of the second array. The method may further include passing a backward pass into the inputs of both of the first and second arrays, and reading voltage values at the inputs of the first and second arrays. The method may further include updating, with the first array, a first matrix update on the first array, updating, with the second array, a first matrix update on the second, and updating, with the second array, a second matrix update on the second array.

Abstract: A computer-implemented method for selecting features for classification may include (1) generating a matrix X, a column vector Y, and a matrix Z from a training dataset that includes a plurality of samples with a plurality of features, (2) generating an augmented matrix from the matrix X, the column vector Y, and the matrix Z, (3) identifying one or more most-relevant features from the plurality of features by iteratively applying a sweep operation to the augmented matrix, and (4) training a classification model using the most-relevant features from the plurality of features rather than all of the plurality of features. Various other methods, systems, and computer-readable media may have similar features.

Abstract: The present disclosure relates to activity monitoring systems and methods for gating whether or not steps should be counted in an observation window based on whether a decision tree concludes there are consecutive step activities (versus no activity or other activities) in the observation window. Particularly, certain aspects are directed to a method that includes obtaining acceleration data for an observation window of an accelerometer, inputting two or more characteristics of the acceleration data into a decision tree to determine activity occurring within the observation window, assigning a first class to the observation window when the determined activity is associated with consecutive steps, assigning a second class to the observation window when the determined activity is not associated with consecutive steps, and when the first class is assigned to the observation window, determining a step count for the observation window using frequency analysis.

Abstract: A decision tree multi-processor system includes a plurality of decision tree processors that access a common feature vector and execute one or more decision trees with respect to the common feature vector. A related method includes providing a common feature vector to a plurality of decision tree processors implemented within an on-chip decision tree scoring system, and executing, by the plurality of decision tree processors, a plurality off decision trees, by reference to the common feature vector. A related decision tree-walking system includes feature storage that stores a common feature vector and a plurality of decision tree processors that access the common feature vector from the feature storage and execute a plurality of decision trees by comparing threshold values of the decision trees to feature values within the common feature vector.

Abstract: An identification of an item that was misclassified by a classification model constructed in accordance with a machine learning technique is received. One example of such a machine learning technique is a random forest. A subset of training data, previously used to construct the model, and that is associated with the item is identified. At least a portion of the identified subset is provided as output.

Abstract: According to an aspect, a term saliency model is trained to identify salient terms that provide supporting evidence of a candidate answer in a question answering computer system based on a training dataset. The question answering computer system can perform term saliency weighting of a candidate passage to identify one or more salient terms and term weights in the candidate passage based on the term saliency model. The one or more salient terms and term weights can be provided to at least one passage scorer of the question answering computer system to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer.

Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module for deploying selected individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding in dependence upon their updated fitness estimate. The system maintains the ancestry count for each of the candidate individuals, and may use this information to adjust the competition among the individuals, to adjust the selection of individuals for further procreation, and/or for other purposes.

Abstract: Technologies are described herein for active machine learning. An active machine learning method can include initiating active machine learning through an active machine learning system configured to train an auxiliary machine learning model to produce at least one new labeled observation, refining a capacity of a target machine learning model based on the active machine learning, and retraining the auxiliary machine learning model with the at least one new labeled observation subsequent to refining the capacity of the target machine learning model. Additionally, the target machine learning model is a limited-capacity machine learning model according to the description provided herein.

Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.

Abstract: Features are disclosed for error tolerant model compression. Such features could be used to reduce the size of a deep neural network model including several hidden node layers. The size reduction in an error tolerant fashion ensures predictive applications relying on the model do not experience performance degradation due to model compression. Such predictive applications include automatic recognition of speech, image recognition, and recommendation engines. Partially quantized models are re-trained such that any degradation of accuracy is “trained out” of the model providing improved error tolerance with compression.

Abstract: Solved diagnosis case data is stored by utilizing a redundant discrimination net as a dynamic memory. The stored diagnosis case data is incorporated to form scientific descriptions within a medical knowledge base and heuristics within an empirical knowledge base. Diagnosis hypotheses are generated using an initial symptom description, the dynamic memory, and the medical knowledge base. The initial symptom description is received from an end user. A subset of the diagnosis hypotheses is created to form one or more solution cases. The one or more solution cases are presented to a subject matter expert. A diagnosis success or a diagnosis failure identifying, based on a response received from the subject matter expert, to form an assessed solution case. An assessed solution case is converted into experiences. The experiences are inputted into the dynamic memory. Data containing the assessed solution case is transmitted to a medical artificial intelligence analytics application.

Abstract: A social networking system identifies users to receive a recommendation to establish a connection to an object maintained by the social networking system. The social networking system determines one or more classifiers identifying attributes of users to receive the recommendation based on attributes of users connected to the object and additional users connected to those users. The attributes of an additional user may be weighted by a factor that provides a measure of the overlap between the attributes of the additional user and a user connected to the object.

Abstract: One embodiment of the invention provides a system for mapping a neural network onto a neurosynaptic substrate. The system comprises a reordering unit for reordering at least one dimension of an adjacency matrix representation of the neural network. The system further comprises a mapping unit for selecting a mapping method suitable for mapping at least one portion of the matrix representation onto the substrate, and mapping the at least one portion of the matrix representation onto the substrate utilizing the mapping method selected. The system further comprises a refinement unit for receiving user input regarding at least one criterion relating to accuracy or resource utilization of the substrate. The system further comprises an evaluating unit for evaluating each mapped portion against each criterion. Each mapped portion that fails to satisfy a criterion may be remapped to allow trades offs between accuracy and resource utilization of the substrate.