Abstract: A method for executing a binarized neural network (BNN) using a switching chip includes describing an artificial neural network application in a binarized form to provide the BNN; configuring a parser of the switching chip to encode an input vector of the BNN in a packet header; configuring a plurality of match-action tables (MATs) of the switching chip to execute, on the input vector encoded in the packet header, one or more of the operations including XNOR, bit counting, and sign operations such that the plurality of MATs are configured to: implement a bitwise XNOR operation between the input vector and a weights matrix to produce a plurality of first stage vectors, implement an algorithm for counting a number of bits set to 1 in the plurality of first stage vectors to produce a plurality of second stage vectors, and implement a sign operation on the second stage vectors.

Abstract: Boltzmann machines are trained using an objective function that is evaluated by sampling quantum states that approximate a Gibbs state. Classical processing is used to produce the objective function, and the approximate Gibbs state is based on weights and biases that are refined using the sample results. In some examples, amplitude estimation is used. A combined classical/quantum computer produces suitable weights and biases for classification of shapes and other applications.

Abstract: The technology disclosed predicts quality of base calling during an extended optical base calling process. The base calling process includes pre-prediction base calling process cycles and at least two times as many post-prediction base calling process cycles as pre-prediction cycles. A plurality of time series from the pre-prediction base calling process cycles is given as input to a trained convolutional neural network. The convolutional neural network determines from the pre-prediction base calling process cycles, a likely overall base calling quality expected after post-prediction base calling process cycles. When the base calling process includes a sequence of paired reads, the overall base calling quality time series of the first read is also given as an additional input to the convolutional neural network to determine the likely overall base calling quality after post-prediction cycles of the second read.

Abstract: A device, system, product and method of controlling resistive processing units (RPUs), includes applying an input voltage signal to each node of an array of resistive processing units, and controlling a learning rate of the array of resistive processing units by varying an amplitude of the input voltage signal to the array of resistive processing units. A conductance state of the array of resistive processing units is varied according to the amplitude received at each of the resistive processing units of the array of resistive processing units. The controlling of the amplitude of input voltage signal is according to a processor of a control device.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Content Optimization Engine that determines a display probability for each content item in a set of content items. Each respective display probability corresponds to a given content item's probability of display in a specific content slot of a plurality of content slots in a social network feed of a target member account in a social network service. The Content Optimization Engine calculates a selection probability for each content item in an ordered set of the content items, based on each display probability and a set of interaction effects. The Content Optimization Engine causes display of the ordered set of content items in the target member account's social network feed based on satisfaction of the first and second targets.

Abstract: Method, systems, crosspoint arrays, and systems for tuning a neural network. A crosspoint array includes: a set of conductive rows, a set of conductive columns intersecting the set of conductive rows to form a plurality of crosspoints, a circuit element coupled to each of the plurality of crosspoints configured to store a weight of the neural network, a voltage source associated with each conductive row, a first integrator attached at the end of at least one of the conductive column, and a first variable resistor attached to the integrator and the end of the at least one conductive column.

Abstract: According to embodiments of the present invention, similarity metrics or measures of similarity may be combined using an adaptive weighting scheme. A subset of entities from a first set of entities that have a known relationship is randomly selected. The subset is combined with a second set of entities that have an unknown relationship to each other and/or to the first set of entities. At least two different measures of similarity (similarity metrics) between the first set and the combined second set (including the subset) is determined for each entity in the second set. For each entity in the second set, the at least two different measures of similarity are compared, and a weight is assigned adaptively to each measure of similarity based on the magnitude of the measure of similarity. The weighted measures of similarity are combined to determine an aggregate adaptively weighted similarity score for each entity.

Abstract: A technique to prune weights of a neural network using an analytic threshold function h(w) provides a neural network having weights that have been optimally pruned. The neural network includes a plurality of layers in which each layer includes a set of weights w associated with the layer that enhance a speed performance of the neural network, an accuracy of the neural network, or a combination thereof. Each set of weights is based on a cost function C that has been minimized by back-propagating an output of the neural network in response to input training data. The cost function C is also minimized based on a derivative of the cost function C with respect to a first parameter of the analytic threshold function h(w) and on a derivative of the cost function C with respect to a second parameter of the analytic threshold function h(w).

Abstract: A method of real time magnetic localization comprising: providing an artificial neural network field model that is calibrated and optimized for a predetermined magnet; receiving signals from one or more magnetic sensors; and solving the location of the magnet using the model based on the signals.

Abstract: Search query result set count estimation is described. A system parses data set query that includes first query attribute and second query attribute. The system identifies first hierarchy of connected nodes including a first node representing a first query attribute, and a second hierarchy of other connected nodes including a second node representing a second query attribute. The system identifies a directed arc connecting first correlated node in first hierarchy to second correlated node in second hierarchy. The system identifies cross-hierarchy probabilities of correlations between values of a first attribute represented by the first correlated node and values of a second attribute represented by the second correlated node.

Abstract: According to embodiments of the present invention, similarity metrics or measures of similarity may be combined using an adaptive weighting scheme. A subset of entities from a first set of entities that have a known relationship is randomly selected. The subset is combined with a second set of entities that have an unknown relationship to each other and/or to the first set of entities. At least two different measures of similarity (similarity metrics) between the first set and the combined second set (including the subset) is determined for each entity in the second set. For each entity in the second set, the at least two different measures of similarity are compared, and a weight is assigned adaptively to each measure of similarity based on the magnitude of the measure of similarity. The weighted measures of similarity are combined to determine an aggregate adaptively weighted similarity score for each entity.

Abstract: A circuit implementing a spiking neural network that includes a learning component that can learn from temporal correlations in the spikes regardless of correlations in the rates. In some embodiments, the learning component comprises a rate-discounting component. In some embodiments, the learning rule computes a rate-normalized covariance (normcov) matrix, detects clusters in this matrix, and sets the synaptic weights according to these clusters. In some embodiments, a synapse with a long-term plasticity rule has an efficacy that is composed by a weight and a fatiguing component. In some embodiments, A Hebbian plasticity component modifies the weight component and a short-term fatigue plasticity component modifies the fatiguing component. The fatigue component increases with increases in the presynaptic spike rate. In some embodiments, the fatigue component increases are implemented in a spike-based manner.

Abstract: A lightened neural network method and apparatus. The neural network apparatus includes a processor configured to generate a neural network with a plurality of layers including plural nodes by applying lightened weighted connections between neighboring nodes in neighboring layers of the neural network to interpret input data applied to the neural network, wherein lightened weighted connections of at least one of the plurality of layers includes weighted connections that have values equal to zero for respective non-zero values whose absolute values are less than an absolute value of a non-zero value. The lightened weighted connections also include weighted connections that have values whose absolute values are no greater than an absolute value of another non-zero value, the lightened weighted connections being lightened weighted connections of trained final weighted connections of a trained neural network whose absolute maximum values are greater than the absolute value of the other non-zero value.

Abstract: Generating insight on a set of data is provided. A request for information regarding a specific topic is received from a client device corresponding to a requester. An analysis is performed on the request and a type of the information requested is determined based on the analysis. A set of information vendors is selected from a plurality of known information vendors based on the type of the information requested and other factors. Insights on the type of the information requested are obtained from the selected set of information vendors and an analysis is performed on the insights. A response to the request is generated based on the analysis of the insights on the type of the information requested that was obtained from the selected set of information vendors. The response to the request is sent to the client device corresponding to the requester.

Abstract: Methods, systems, and apparatus for prioritizing communications are described. Metadata that characterizes an electronic communication is obtained and a machine learning algorithm is applied to the metadata to generate a scoring model. A score for the electronic communication is generated based on the scoring model.

Abstract: At a machine learning service, an indication of a training data set for a model is obtained. One or more training iterations of the model are conducted using an adaptive input sampling strategy. In a particular iteration, index values for a set of training observations are selected based on a set of sampling weights, parameters of the model are updated based on results using training observations identified by the index values, and sampling weights are modified. A result obtained from a trained version of the machine learning model is provided.

Abstract: Various embodiments of the present invention relate generally to systems and processes for transforming a style of video data. In one embodiment, a neural network is used to interpolate native video data received from a camera system on a mobile device in real-time. The interpolation converts the live native video data into a particular style. For example, the style can be associated with a particular artist or a particular theme. The stylized video data can viewed on a display of the mobile device in a manner similar to which native live video data is output to the display. Thus, the stylized video data, which is viewed on the display, is consistent with a current position and orientation of the camera system on the display.

Abstract: A capability to identify and visualize relationships and commonalities amongst record entities is provided. A plurality of entities are extracted from one or more records. Each extracted entity is associated with a respective feature vector within a vector space of a feature matrix. The feature vectors are distributed within the feature matrix based on semantic relationships amongst the entities of a corpus. Multidimensional coordinates within a dimensionally-reduced vector space of the feature matrix are generated for each extracted entity. One or more cells of a cellular presentation of the feature matrix are identified such that each identified cell represents one or more respective extracted entities. Each cell represents (i) a respective range of multidimensional coordinates within the dimensionally-reduced vector space of the feature matrix and (ii) one or more feature vectors of the plurality of feature vectors within the feature matrix.

Abstract: Methods and apparatuses are described for generation of a data model for identifying and classifying training needs of individuals. A computer data store stores unstructured text. A server computing device generates a vector for search queries in the unstructured text, and generates a training course classification data model that comprises a multi-layered neural network. The server computing device executes the training course classification model using the vectors as input to generate a training course recommendation output vector. The server computing device updates the training course classification data model based upon a rating value for a training course.

Abstract: The disclosure below describes a knowledge pattern machine that goes beyond and is distinct from a traditional search engine as simple information aggregator. Rather than acting as a search engine of the data itself, the knowledge pattern machine use variously layers of artificial intelligence to discover correlations within the queries and historical data, and to derive and recognize data patterns based on user queries for predictively generating new knowledge items or reports that are of interest to the user. Previous patterns and knowledge items or reports are accumulated and incorporated in identification of new data patterns and new predictive knowledge items or reports in response to future user queries, thus providing a stateful machine. The predictive knowledge items are updated in real-time without user interference as the underlying data sources evolve overtime.