Abstract: A color predictor is provided to predict the color of a food item given its formula comprising the ingredients and its quantities. The color predictor may utilize machine learning algorithms and a set of recipe data to train the color predictor. The color predictor can also be used by a color recommender to recommend changes in the given formula to achieve a target color.

Abstract: System and method embodiments are provided for adaptive anomaly detection based predictor for network data. In an embodiment, a computer-implemented method in a network component for predicting values of future network time series data includes receiving, with one or more receivers, network time series data; determining, with one or more processors, whether an anomaly is detected in the network time series data; generating, with the one or more processors, a prediction associated with the network data according to a primary predictor when no anomaly is detected in the network time series data; generating, with the one or more processors, the prediction associated with the network data according to an alternative predictor when an anomaly in the network time series data is detected; and sending, with one or more transmitters, the prediction to a network controller, wherein the network controller uses the prediction to adjust network parameters.

Abstract: A tool computes fitness values for a first generation of a first sub-population of a plurality of sub-populations. A population of candidate solutions for an optimization problem was previously divided into the plurality of sub-populations. The population of candidate solutions was created for an iterative computing process in accordance with an evolutionary algorithm to identify a most fit candidate solution for the optimization problem. The tool determines a speculative ranking of the first generation of the first sub-population prior to the fitness values being computed for all candidate solutions in the first generation of the first sub-population. The tool generates a next generation of the first sub-population based, at least in part, on the speculative ranking prior to completion of computation of the fitness values for the first generation of the first sub-population.

Abstract: Systems and methods for projecting one or more trends in electronic data and generating enhanced data. A system includes a data forecasting system is in electronic communication with one or more electronic data sources via an electronic network. The data forecasting system is configured to: monitor the electronic data source(s) for data that meet one or more predetermined criteria; obtain at least a portion of the monitored data from electronic data source(s) based on the predetermined criteria; create a data set from the obtained data; derive one or more data values associated with the data set over a predetermined period according to a forward-looking term methodology; and utilize the data set and the derived value(s) over the predetermined period to derive at least one data forecast metric associated with the data set.

Abstract: A system and method may be used to evaluate content on one or more social media networks. A deep learning model may be stored. A communication may be received, that has been or is to be communicated on a social network. The deep learning model may be applied to the communication to obtain an automated evaluation of the communication. User input may be received, and may include a user evaluation of the communication. The user evaluation may be applied to train the deep learning model. The steps of receiving the communication, applying the deep learning model to obtain the automated evaluation, receiving the user evaluation, and applying the user evaluation to train the model, may be iterated to enhance the accuracy of the automated evaluations.

Abstract: An artificial neuron includes a signal mixer that combines input signals to provide a first stochastic bit-stream as output and a stochastic activation function circuit configured to receive the first stochastic bit-stream from the signal mixer and to generate therefrom a second stochastic bit-stream. The first stochastic bit-stream is representative of a first output value. In the stochastic activation function circuit, n independent stochastic bit-streams, each representative of the first output value, are summed to provide a selection signal that is provided to a multiplexer to select between n+1 coefficient bit-streams and provide the second stochastic bit-stream. The activation function has a characteristic determined by the proportion of ones in each of the n+1 coefficients bit-streams. One or more artificial neurons may be used in an Artificial Neural Network, such as a Time Delay Reservoir network.

Abstract: A device, system, and method for approximating a neural network comprising N synapses or filters. The neural network may be partially-activated by iteratively executing a plurality of M partial pathways of the neural network to generate M partial outputs, wherein the M partial pathways respectively comprise M different continuous sequences of synapses or filters linking an input layer to an output layer. The M partial pathways may cumulatively span only a subset of the N synapses or filters such that a significant number of the remaining the N synapses or filters are not computed. The M partial outputs of the M partial pathways may be aggregated to generate an aggregated output approximating an output generated by fully-activating the neural network by executing a single instance of all N synapses or filters of the neural network. Training or prediction of the neural network may be performed based on the aggregated output.

Abstract: Embodiments of the invention relate to a globally asynchronous and locally synchronous neuromorphic network. One embodiment comprises generating a synchronization signal that is distributed to a plurality of neural core circuits. In response to the synchronization signal, in at least one core circuit, incoming spike events maintained by said at least one core circuit are processed to generate an outgoing spike event. Spike events are asynchronously communicated between the core circuits via a routing fabric comprising multiple asynchronous routers.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.

Abstract: Methods and Systems for automatic information extraction by performing self-learning crawling and rule-based data mining is provided. The method determines existence of crawl policy within input information and performs at least one of front-end crawling, assisted crawling and recursive crawling. Downloaded data set is pre-processed to remove noisy data and subjected to classification rules and decision tree based data mining to extract meaningful information. Performing crawling techniques leads to smaller relevant datasets pertaining to a specific domain from multi-dimensional datasets available in online and offline sources.

Abstract: A mechanism is provided for automatic genre determination of web content. For each type of web content genre, a set of relevant feature types are extracted from collected training material, where genre features and non-genre features are represented by tokens and an integer counts represents a frequency of appearance of the token in both a first type of training material and a second type of training material. In a classification process, fixed length tokens are extracted for relevant features types from different text and structural elements of web content. For each relevant feature type, a corresponding feature probability is calculated. The feature probabilities are combined to an overall genre probability that the web content belongs to a specific trained web content genre. A genre classification result is then output comprising at least one specific trained web content genre to which the web content belongs together with a corresponding genre probability.

Abstract: Systems, methods, and media for extracting and processing entity data included in an electronic document are provided herein. Methods may include executing one or more extractors to extract entity data within an electronic document based upon an extraction model for the document, selecting extracted entity data via one or more experts, each of the experts applying at least one business rule to organize at least a portion of the selected entity data into a desired format, and providing the organized entity data for use by an end user.

Abstract: This disclosure relates generally to performing user segmentation, and more particularly to predicting attribute values for user segmentation. In one embodiment, the method includes segregating a user with an incomplete attribute value and a user with complete attribute values for an attribute into a first group and a second group respectively, computing prior probability for each suggestive attribute value, identified for the incomplete attribute value, based on number of users in second group having the suggestive attribute value as attribute value for the attribute. Computing likelihood for each suggestive attribute value based on similarity of the attribute values of the user of the first group with users of the second group, computing a posterior probability for each suggestive attribute value based on the prior probability and the likelihood, selecting a suggestive attribute value with the highest posterior probability as the attribute value for the incomplete attribute value of the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for acoustic modeling of audio data. One method includes receiving audio data representing a portion of an utterance, providing the audio data to a trained recurrent neural network that has been trained to indicate the occurrence of a phone at any of multiple time frames within a maximum delay of receiving audio data corresponding to the phone, receiving, within the predetermined maximum delay of providing the audio data to the trained recurrent neural network, output of the trained neural network indicating a phone corresponding to the provided audio data using output of the trained neural network to determine a transcription for the utterance, and providing the transcription for the utterance.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network. One of the methods includes maintaining a replay memory that stores trajectories generated as a result of interaction of an agent with an environment; and training an action selection neural network having policy parameters on the trajectories in the replay memory, wherein training the action selection neural network comprises: sampling a trajectory from the replay memory; and adjusting current values of the policy parameters by training the action selection neural network on the trajectory using an off-policy actor critic reinforcement learning technique.

Abstract: Incremental model training for advertisement targeting is performed using streaming data. A model for targeting advertisements of an advertising campaign is initialized. A data stream including data corresponding to converters and data corresponding to non-converters is received. The model is then applied to the data corresponding to the converter and data corresponding to the non-converter (or other ratio of converter to non-converters) to obtain a predicted score for each. The predicted score is compared to the observed score (e.g., an observed score of 1 for a converter, and 0 for a non-converter). The difference between the predicted and observed scores is computed, and the model is incrementally updated based on this difference. Models can optionally be built separately on multiple modeling servers that are geographically dispersed in order to support bidding on advertising opportunities in a real-time bidding environment.

Abstract: A connectivity look up structure is maintained for a network that comprises a plurality of nodes, each node is connectable to one or more other nodes, and nodes that are connected tend to be local to one another in the network, and the number of node connections in the network tends to be sparse in relation to the number of potential node connections in the network. The connectivity look up structure stores, for a given node, an address of each other node that is connected to the given node, wherein the stored address for the other node is represented as a run-length encoded difference between a full network address of the given node and a full network address of the other node.

Abstract: A computer-based system and method for developing an optimized step-by-step procedure for servicing a monitored machine using case-based reasoning based on an analysis of stored machine specifications (including warranty information) and using other rules based on an analysis of sensor data received through a telematics system from sensors equipped on the machine. The system generates an optimized service procedure based on previously collected and real time information to enable service to be performed more efficiently.

Abstract: Embodiments of the invention provide a neurosynaptic system comprising a delay unit for receiving and buffering axonal inputs, and a neural computation unit for generating neuronal outputs by performing a set of computations based on at least one axonal input received by the delay unit. The system further comprises a permutation unit for receiving external inputs to the system, and transmitting external outputs from the system. The permutation unit maps each external input received as either an axonal input to the delay unit or an external output from the system. The permutation unit maps each neuronal output generated by the neural computation unit as either an axonal input to the delay unit or an external output from the system. The neural computation unit comprises multiple electronic neurons, multiple electronic axons, and a plurality of electronic synapse devices interconnecting the neurons with the axons.

Abstract: A synaptic circuit performing spike-timing dependent plasticity STDP interposed between a pre-synaptic neuron and a post-synapse neuron includes a memristor having a variable resistance value configured to receive a first signal from the pre-synaptic neuron. The circuit has an intermediate unit connected in series with the memristor for receiving a second signal from the pre-synaptic neuron and provides an output signal to the post-synaptic neuron. The intermediate unit receives a retroaction signal generated from the post-synaptic neuron and the memristor modifies the resistance value based on a delay between two at least partially overlapped input pulses, a spike event of the first signal and a pulse of the retroaction signal, in order to induct a potentiated state STP or a depressed state STD at the memristor. An electronic neuromorphic system having synaptic circuits and a method of performing spike timing dependent plasticity STDP by a synaptic circuit are also provided.