Abstract: [Problem] To provide design support useful for hardware implementation of a neural network. [Solution] Provided is an information processing device including a control unit that controls presentation of information related to an optimization structure when at least a part of a network structure of a designed neural network has been optimized for hardware processing. Provided is an information processing method including a step of controlling, by a processor, presentation of information related to an optimization structure when at least a part of a network structure of a designed neural network has been optimized for hardware processing.

Abstract: An information processing apparatus is provided which includes a sensor information acquisition unit that acquires sensor information including at least biological information of a user and a biological information change prediction unit that predicts a change of the biological information from the sensor information in accordance with a framework based on knowledge concerning poor physical condition of the user.

Abstract: It is aimed to facilitate obtaining of a large number of pieces of data for learning that are necessary to obtain a good-quality learning result. A feature value of a first dataset is compared with feature values of a predetermined number of second datasets. A determination as to whether or not each of the predetermined number of second datasets is a dataset usable together with the first dataset is made on the basis of the result of the comparison. For example, the determination is made referring to lacking data information associated with the first dataset. For example, information regarding a second dataset having been determined to be the dataset usable together with the first dataset is presented.

Abstract: To reduce processing load of an operation and to realize learning with higher accuracy. There is provided an information processing device including a learning unit that optimizes parameters that determine a dynamic range by an error back propagation and a stochastic gradient descent in a quantization function of a neural network in which the parameters that determine the dynamic range are arguments. There is provided an information processing method, by a processor, including optimizing parameters that determine a dynamic range by an error back propagation and a stochastic gradient descent in a quantization function of a neural network in which the parameters that determine the dynamic range are arguments.

Abstract: There is provided an information processing apparatus and an information processing method to present information for improving development efficiency of a neural network to a user. The information processing method includes: providing, by a processor, a form for creating a program for establishing a neural network on a basis of a disposed component and property set for the component; and presenting statistical information relating to the neural network. The information processing apparatus includes a form control unit configured to provide a form for creating a program for establishing a neural network on a basis of a disposed component and property set for the component. The form control unit presents statistical information relating to the neural network.

Abstract: [Problem] To provide design support useful for hardware implementation of a neural network. [Solution] Provided is an information processing device including a control unit that controls presentation of information related to an optimization structure when at least a part of a network structure of a designed neural network has been optimized for hardware processing. Provided is an information processing method including a step of controlling, by a processor, presentation of information related to an optimization structure when at least a part of a network structure of a designed neural network has been optimized for hardware processing.

Abstract: There is provided an information processing apparatus, an information processing method, and a program for enabling quantization with high accuracy. Quantization is performed assuming that a distribution of values calculated by a machine learning operation is based on a predetermined probability distribution. The operation is an operation in deep learning, and the quantization is performed on the basis of a notion that a distribution of gradients calculated by the operation based on the deep learning is based on the predetermined probability distribution. The quantization is performed when a value obtained by learning in one apparatus is supplied to another apparatus in distributed learning in which machine learning is performed by a plurality of apparatuses in a distributed manner. The present technology can be applied to an apparatus that performs machine learning such as deep learning in a distributed manner.

Abstract: There is provided an information processing apparatus and an information processing method to present information for improving development efficiency of a neural network to a user. The information processing method includes: providing, by a processor, a form for creating a program for establishing a neural network on a basis of a disposed component and property set for the component; and presenting statistical information relating to the neural network. The information processing apparatus includes a form control unit configured to provide a form for creating a program for establishing a neural network on a basis of a disposed component and property set for the component. The form control unit presents statistical information relating to the neural network.

Abstract: There is provided an information processing apparatus and an information processing method to present information for improving development efficiency of a neural network to a user. The information processing method includes: comparing, by a processor, learning results by a plurality of models of neural networks and presenting comparison information relating to the learning results using a graph. In addition, an information processing apparatus includes a control unit configured to acquire learning results by a plurality of models of neural networks and control comparison information relating to the learning results. The control unit presents the comparison information using a graph. In addition, an information processing apparatus includes a comparing unit configured to compare learning results by a plurality of models of neural networks and generate comparison information relating to the learning results. The comparison information is comparison information using a graph.

Abstract: A computer-implemented method of generating a derived artificial neural network (ANN) from a base ANN comprises initialising a set of parameters of the derived ANN in dependence upon parameters of the base ANN; inferring a set of output data from a set of input data using the base ANN; quantising the set of output data; and training the derived ANN using training data comprising the set of input data and the quantised set of output data.

Abstract: A computer-implemented method of generating a modified artificial neural network (ANN) from a base ANN having an ordered series of two or more successive layers of neurons, each layer passing data signals to the next layer in the ordered series, the neurons of each layer processing the data signals received from the preceding layer according to an activation function and weights for that layer comprises: detecting the data signals for a first position and a second position in the ordered series of layers of neurons; generating the modified ANN from the base ANN by providing an introduced layer of neurons to provide processing between the first position and the second position with respect to the ordered series of layers of neurons of the base ANN; deriving an initial approximation of at least a set of weights for the introduced layer using a least squares approximation from the data signals detected for the first position and a second position; and processing training data using the modified ANN to train the

Abstract: Disclosed is an information processing apparatus including an expression extraction unit, a feature extraction unit, a clustering unit, a related expression extraction unit, and an output unit. The expression extraction unit extracts a plurality of expressions from a plurality of documents. The feature extraction unit extracts feature amounts of the extracted respective expressions while distinguishing the expressions having the same notation. The clustering unit clusters the extracted respective expressions together while distinguishing the expressions having the same notation and calculates assignment degree vectors having assignment degrees of the respective expressions to two or more respective clusters as components. The related expression extraction unit extracts related expressions having the assignment degree vectors similar to those of a provided input expression while distinguishing the expressions having the same notation.

Abstract: A computer-implemented method of training an artificial neural network (ANN) by generating a first learned parameter for use in normalising input data values during a subsequent inference phase of the trained ANN. The method includes, for each of a series of batches of training data values, deriving a batch variance of the batch of training data values and a running variance of all training data values already processed in the training phase; generating an approximation of a current value of the first learned parameter so that a first scaling factor dependent upon the approximation of the first learned parameter and the running variance, is constrained to be equal to a power of two; and normalizing the batch of input data values by a second scaling factor dependent upon the approximation of the current value of the first learned parameter and the batch variance.

Abstract: There is provided an information processing apparatus and an information processing method to present information for improving development efficiency of a neural network to a user. The information processing method includes: comparing, by a processor, learning results by a plurality of models of neural networks and presenting comparison information relating to the learning results using a graph. In addition, an information processing apparatus includes a control unit configured to acquire learning results by a plurality of models of neural networks and control comparison information relating to the learning results. The control unit presents the comparison information using a graph. In addition, an information processing apparatus includes a comparing unit configured to compare learning results by a plurality of models of neural networks and generate comparison information relating to the learning results. The comparison information is comparison information using a graph.

Abstract: An apparatus comprising circuitry that implements an artificial neural network training algorithm that uses weight tying.

Abstract: Disclosed is an information processing apparatus including a metadata expansion unit. The metadata expansion unit is configured to expand metadata of a target item using metadata of other items to which a responder has shown a response, the responder being a user having shown a response to the target item of which the metadata is to be expanded.

Abstract: [Object] To be able to predict a change in condition of a user in a wider range. [Solution] Provided is an information processing apparatus including: a sensor information acquisition unit configured to acquire sensor information including at least biological information of a user; and a biological information change prediction unit configured to predict a change of the biological information from the sensor information in accordance with a framework based on knowledge concerning poor physical condition of the user.

Abstract: There is provided an information processing apparatus including an obtaining unit configured to obtain one or more other information elements corresponding to one information element based on a degree of association between the one information element and the other information elements, an extraction unit configured to extract a description of a type of association between the one information element and the obtained other information elements, and a presentation control unit configured to perform control in a manner that the one information element, the other information elements, the type of association, and the description of the type of association are presented.

Abstract: Disclosed is an information processing apparatus including an expression extraction unit, a feature extraction unit, a clustering unit, a related expression extraction unit, and an output unit. The expression extraction unit extracts a plurality of expressions from a plurality of documents. The feature extraction unit extracts feature amounts of the extracted respective expressions while distinguishing the expressions having the same notation. The clustering unit clusters the extracted respective expressions together while distinguishing the expressions having the same notation and calculates assignment degree vectors having assignment degrees of the respective expressions to two or more respective clusters as components. The related expression extraction unit extracts related expressions having the assignment degree vectors similar to those of a provided input expression while distinguishing the expressions having the same notation.

Abstract: Disclosed is an information processing apparatus including a metadata expansion unit. The metadata expansion unit is configured to expand metadata of a target item using metadata of other items to which a responder has shown a response, the responder being a user having shown a response to the target item of which the metadata is to be expanded.