Abstract: One or more computing devices, systems, and/or methods for generating time-preserving embeddings are provided. User trails of user activities performed by users are generated. Frequencies at which the activities were performed are identified. Indices are assigned to a set of activities identified from the activities as having frequencies above a threshold. Activity descriptions of the set of activities are mapped to the indices to generate a vocabulary. A model is trained using the user trails, timestamps of the activities, and the vocabulary to learn a set of time-preserving embeddings.

Abstract: Hardware implementations of, and methods for processing, a convolution layer of a DNN that comprise a plurality of convolution engines wherein the input data and weights are provided to the convolution engines in an order that allows input data and weights read from memory to be used in at least two filter-window calculations performed either by the same convolution engine in successive cycles or by different convolution engines in the same cycle. For example, in some hardware implementations of a convolution layer the convolution engines are configured to process the same weights but different input data each cycle, but the input data for each convolution engine remains the same for at least two cycles so that the convolution engines use the same input data in at least two consecutive cycles.

Abstract: A method of modelling data, comprising: training an objective function of a linear classifier, based on a set of labeled data, to derive a set of classifier weights; defining a posterior probability distribution on the set of classifier weights of the linear classifier; approximating a marginalized loss function for an autoencoder as a Bregman divergence, based on the posterior probability distribution on the set of classifier weights learned from the linear classifier; and classifying unlabeled data using the autoencoder according to the marginalized loss function.

Abstract: According to an embodiments of the present disclosure, a method comprising: at an electronic device with one or more processors, obtaining a data set; identifying, based on the data set, a first data point set on a first embedding space, wherein each data point included in the first data point set corresponds to each data included in the data set; identifying a modified first data point set on the first embedding space based on the first data point set by adjusting a property associated with a distribution of the first data point set, wherein the modified first data point set includes at least one modified data point which is not included in the first data point set; and providing a Modified Image of Data (MIOD) by representing the modified first data point set on an imaging space may be provided.

Abstract: Disclosed is a low-power and compact neuron circuit implementing a ReLU activation function including a first-layer synaptic array, a neuron transistor, a resistor, and a second-layer synaptic array. The neuron transistor is a MOS transistor having a threshold voltage-adjustable property, a gate electrode of the neuron transistor is connected to each voltage output end of the first-layer synaptic array, and a drain electrode of the neuron transistor is connected to each voltage input end of the second-layer synaptic array. Thus, it is possible to satisfy the decision computation and output of different synaptic array output values by adjusting the magnitude of the threshold voltage of the transistor. The neuron circuit requires only one transistor in cooperative connection with the first-layer synaptic array and the second-layer synaptic array to implement the ReLU activation function; therefore, a significant improvement is achieved in terms of energy efficiency, delay reduction, and space utilization.

Abstract: In one aspect, the invention comprises a system and method for control of a transaction state system utilizing a distributed ledger. First, the system and method includes an application plane layer adapted to receive instructions regarding operation of the transaction state system. Preferably, the application plane layer is coupled to the application plane layer interface. Second, a control plane layer is provided, the control plane layer including an adaptive control unit, such as a cognitive computing unit, artificial intelligence unit or machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources and to provide output coupled to a decentralized distributed ledger, the data plane layer is coupled to the control plane layer. Optionally, the system and method serve to implement a smart contract on a decentralized distributed ledger.

Abstract: In training a deep neural network using reduced precision, gradient computation operates on larger values without affecting the rest of the training procedure. One technique trains the deep neural network to develop loss, scales the loss, computes gradients at a reduced precision, and reduces the magnitude of the computed gradients to compensate for scaling of the loss. In one example non-limiting arrangement, the training forward pass scales a loss value by some factor S and the weight update reduces the weight gradient contribution by 1/S. Several techniques can be used for selecting scaling factor S and adjusting the weight update.

Abstract: A processor-implemented neural network method includes: obtaining a first weight kernel of a weight model and pruning information of the first weight kernel; determining, based on the pruning information, a processing range of an input feature map for each weight element vector of the first weight kernel; performing a convolution operation between the input feature map and the first weight kernel based on the determined processing range; and generating an output feature map of a neural network layer based on an operation result of the convolution operation.

Abstract: Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Abstract: The present disclosure provides a composite machine learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine learning model is updated based on the descriptive string and the label. The machine learning model is then trained against the updated set of training data.

Abstract: According to an embodiment, an information processing system solves a combinatorial optimization problem. The information processing system includes an Ising machine and a host unit. The Ising machine is hardware configured to perform a search process for searching for the ground state of an Ising model that represents the combinatorial optimization problem. The host unit is hardware connected to the Ising machine via an interface and configured to control the Ising machine. In the search process, for each of a plurality of Ising spins, the Ising machine alternately repeats an auxiliary variable update process for updating an auxiliary variable by a main variable and a main variable update process for updating the main variable by the auxiliary variable multiple times. Prior to the search process, the host unit transmits, to the Ising machine, an initial value of the auxiliary variable corresponding to each of the plurality of Ising spins.

Abstract: A data processing apparatus configured to execute hierarchical calculation processing corresponding to a neural network on input data includes a storage unit configured to store a plurality of sets of control data each for use for one of a plurality of processing units into which the hierarchical calculation processing corresponding to the neural network is divided, a transfer unit configured to sequentially transfer the plurality of sets of control data from the storage unit, and a calculation processing unit configured to perform calculation processing of the processing unit corresponding to one set of control data transferred by the transfer unit using the one set of control data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned identification of radio frequency (RF) signals.

Abstract: A method for applying a style to an input image to generate a stylized image. The method includes maintaining data specifying respective parameter values for each image style in a set of image styles, receiving an input including an input image and data identifying an input style to be applied to the input image to generate a stylized image that is in the input style, determining, from the maintained data, parameter values for the input style, and generating the stylized image by processing the input image using a style transfer neural network that is configured to process the input image to generate the stylized image.

Abstract: A computer-implemented method is disclosed for a first (P(TM)) to gain knowledge. The method includes: performing a first P(TM(i)) to interact with a P(TM(thing)) to set a first Thing that is representative of content, performing a second P(TM(i)) to interact with the P(TM(thing)) to parse the content of the first Thing as a second Thing that is representative of a statement, performing a third P(TM(i)) to interact with the P(TM(thing)) to evaluate the statement of the second Thing to compute a third Thing that is representative of a performable statement, and performing a fourth P(TM(i)) to interact with the P(TM(thing)) to perform the performable statement of the third Thing, The fourth P(TM(i)), in performing the performable statement, interacts with P(TM(thing)) to set one or more Things that are representative of posterior knowledge.

Abstract: A machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system.

Abstract: Systems, methods, and frameworks for detecting cryptocurrency wallet artifacts in a file system of a device are provided. The cryptocurrency wallet artifacts can be automatically detected and can include: (i) cryptocurrency wallet application folders; (ii) images containing cryptocurrency artifacts (e.g., mnemonics phrases and/or transactions information); and/or (iii) web browsers artifacts (e.g., cache data, credentials, cookies, and/or bookmarks). This information can be analyzed and extracted using machine learning (ML), natural language processing (NLP), a convolution neural network (CNN), a recurrent neural network (RNN), and/or a string search algorithm.

Abstract: According to an embodiments of the present disclosure, a method comprising: at an electronic device with one or more processors, obtaining a data set; identifying, based on the data set, a first data point set on a first embedding space, wherein each data point included in the first data point set corresponds to each data included in the data set; identifying a modified first data point set on the first embedding space based on the first data point set by adjusting a property associated with a distribution of the first data point set, wherein the modified first data point set includes at least one modified data point which is not included in the first data point set; and providing a Modified Image of Data (MIOD) by representing the modified first data point set on an imaging space may be provided.

Abstract: Aspects of the disclosure generally relate to computing devices and may be generally directed to devices, systems, methods, and/or applications for learning conversations among two or more conversation participants, storing this knowledge in a knowledgebase (i.e. neural network, graph, sequences, etc.), and enabling a user to simulate a conversation with an artificially intelligent conversation participant.