Abstract: An information processing method implemented by a computer includes: executing a generation processing that includes generating a first mini-batch by performing data extension processing on learning data and processing to generate a second mini-batch without performing the data extension processing on the learning data; and executing a learning processing by using a neural network, the learning processing being configured to perform first learning by using the first mini-batch, and then perform second learning by using the second mini-batch.

Abstract: An arithmetic processing device includes: a memory; and a processor coupled to the memory and configured to: execute a plurality of data processes each of which is divided into a plurality of pipeline stages in parallel at different timings; measure a processing time of each of the plurality of pipeline stages; and set a priority of the plurality of pipeline stages in a descending order of the measured processing time.

Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes inputting training data to a machine learning model that includes a generator and a discriminator, the generator generating second input data in which a part of first input data is rewritten in response to an input of the first input data, the discriminator discriminating a rewritten portion in response to an input of the second input data generated by the generator, generating correct answer information, based on the training data and an output result of the generator, and executing training of the machine learning model by using first error information obtained based on the output result of the generator and a discrimination result of the discriminator, and second error information obtained based on the discrimination result of the discriminator and the correct answer information.

Abstract: A non-transitory computer-readable storage medium storing a machine learning program that causes at least one computer to execute a process, the process includes acquiring a first training rate of a first layer that is selected to stop training among layers included in a machine learning model during training of the machine learning model; setting a first time period to stop training the first layer based on the training rate; and training the first layer with controlling the training rate up to the first time period.

Abstract: A process includes, wherein a subset of elements of first training-data that includes elements is masked in second training-data, generating, from the second training-data, third training-data in which a subset of elements of data that includes output of a generator that estimates an element appropriate for a masked-portion in the first training-data and a first element other than the masked-portion in the second training-data is masked, and updating a parameter of a discriminator, which identifies whether the first element out of the third training-data replaces an element of the first training-data and which estimates an element appropriate for the masked-portion in the third training-data, so as to minimize an integrated loss function obtained by integrating first and second loss functions that are calculated based on output of the discriminator and the first training-data and that are respectively related to an identification result and an estimation result of the discriminator.

Abstract: A computer-implemented method of a speed-up processing, the method including: calculating variance of weight information regarding a weight updated by machine learning, for each layer included in a machine learning model at a predetermined interval at time of the machine learning of the machine learning model; and determining a suppression target layer that suppresses the machine learning on the basis of a peak value of the variance calculated at the predetermined interval and the variance of the weight information calculated at the predetermined interval.

Abstract: An arithmetic processing device includes: a memory; and a processor coupled to the memory and configured to: execute a plurality of data processes each of which is divided into a plurality of pipeline stages in parallel at different timings; measure a processing time of each of the plurality of pipeline stages; and set a priority of the plurality of pipeline stages in a descending order of the measured processing time.

Abstract: Each of a plurality of processors enters, to a model representing a neural network and including a common first weight, first data different from that used by the other processors, calculates an error gradient for the first weight, and integrates the gradients calculated by each processor. Each processor stores the first weight in a memory and updates the weight of the model to a second weight based on a hyperparameter value different from those used by the other processors, the integrated error gradient, and the first weight. Each processor enters common second data to the model, compares the evaluation results acquired by each processor, and selects a common hyperparameter value. Each processor updates the weight of the model to a third weight based on the selected hyperparameter value, the integrated error gradient, and the first weight stored in the memory.

Abstract: An information processing apparatus for controlling a plurality of nodes mutually coupled via a plurality of cables, the apparatus includes: a memory; a processor coupled to the memory, the processor being configured to cause a first node to execute first processing to extract coupling relationship between the plurality of nodes, the first node being one of the plurality of nodes, being sequentially allocated from each of the plurality of nodes, the first processing including executing allocation processing that allocates unique coordinate information to the first node and allocates common coordinate information to nodes excluding the first node; executing transmission processing that causes the first node to transmit first information to each of the cables coupled to the first node; and executing identification processing that identifies a node having received the first information as neighboring node coupled to one of the plurality of cables coupled to the first node.

Abstract: A method of controlling an information processing apparatus, the information processing apparatus being configured to perform learning processing by using a neural network, the method includes: executing a calculation processing that includes calculating a learning rate, the learning rate being configured to change in the form of a continuous curve such that the time from when the learning rate is at an intermediate value of a maximum value to when the learning rate reaches a minimum value is shorter than the time from when the learning processing starts to when the learning rate reaches the intermediate value of the maximum value; and executing a control processing that includes controlling, based on the calculated learning rate, an amount of update at the time when a weight parameter is updated in an update processing.

Abstract: A non-transitory computer-readable recording medium storing an analysis program that causes a computer to execute a process, the process includes combining an artificial intelligence (AI) system with a plurality of deep learning models; and creating accuracy reference information for the plurality of deep learning models in a space in which accuracy evaluation information is projected in multiple dimensions, by using discrete threshold value evaluations, for the AI system.

Abstract: A non-transitory computer-readable recording medium storing a calculation processing program for causing a computer to execute processing, the processing including: calculating error gradients of a plurality of layers of a machine learning model that includes an input layer of the machine learning model at the time of machine learning of the machine learning model; selecting a layer of which the error gradient is less than a threshold as a suppression target of the machine learning; and controlling a learning rate and performing the machine learning on the layer selected as the suppression target in a certain period of time before the machine learning is suppressed.

Abstract: A computer-implemented method includes: calculating error gradients with respect to a plurality of layers included in a machine learning model at a time of machine learning of the machine learning model, the plurality of layers including an input layer of the machine learning model; specifying, as a layer to be suppressed, a layer located in a range from a position of the input layer to a predetermined position among the layers in which the error gradient is less than a threshold; and suppressing the machine learning for the layer to be suppressed.

Abstract: A method for controlling an information processing system, the information processing system including multiple information processing devices coupled to each other, each of the multiple information processing devices including multiple main operation devices and multiple aggregate operation devices that are coupled to each other, the method includes: acquiring, by each of the aggregate operation devices, array data items from a main operation device coupled to the concerned aggregate operation device; determining the order of dimensions in which a process is executed and in which the information processing devices are coupled to each other; executing for each of the dimensions in accordance with the order of the dimensions, a process of halving the array data items and distributing the array data items to information processing devices arranged in the dimension; executing a process of transmitting, to information processing devices arranged in the dimension, operation results calculated based on data items.

Abstract: An information processing method executed by a computer, the method includes inputting training data to a machine learning model that includes a convolution layer and acquire an output result by the machine learning model; extracting a specific element that meets a specific condition from among elements included in error information based on an error between the training data and the output result; and performing machine learning of the convolution layer using the specific element.

Abstract: A process includes starting a learning process for building a model including multiple layers each including a parameter. The learning process executes iterations, each including calculating output error of the model using training data and updating the parameter value based on the output error. The process also includes selecting two or more candidate layers representing candidates for layers, where the updating is to be suppressed, based on results of a first iteration of the learning process. The process also includes calculating, based on the number of iterations executed up to the first iteration, a ratio value which becomes larger when the number of iterations executed is greater, and determining, amongst the candidate layers, one or more layers, where the updating is to be suppressed at a second iteration following the first iteration. The number of one or more layers is determined according to the ratio value.

Abstract: A memory holds a model including a plurality of layers including their respective parameters and training data. A processor starts learning processing, which repeatedly calculates an error of an output of the model by using the training data, calculates an error gradient, which indicates a gradient of the error with respect to the parameters, for each of the layers, and updates the parameters based on the error gradients. The processor calculates a difference between a first error gradient calculated in a first iteration in the learning processing and a second error gradient calculated in a second iteration after the first iteration for a first layer among the plurality of layers. In a case where the difference is less than a threshold, the processor skips the calculating of the error gradient and the updating of the parameter for the first layer in a third iteration after the second iteration.

Abstract: A first learning rate is set to a first block including a first parameter, and a second learning rate, which is smaller than the first learning rate, is set to a second block including a second parameter. The first block and the second block are included in a model. Learning processing in which updating the first parameter based on a prediction error of the model, the prediction error having been calculated by using training data, and the first learning rate and updating the second parameter based on the prediction error and the second learning rate are performed iteratively is started. An update frequency of the second parameter is controlled such that this update frequency becomes lower than an update frequency of the first parameter by intermittently omitting the updating of the second parameter in the learning processing based on a relationship between the first and second learning rates.

Abstract: A method of controlling an information processing apparatus, the information processing apparatus being configured to perform learning processing by using a neural network, the method includes: executing a calculation processing that includes calculating a learning rate, the learning rate being configured to change in the form of a continuous curve such that the time from when the learning rate is at an intermediate value of a maximum value to when the learning rate reaches a minimum value is shorter than the time from when the learning processing starts to when the learning rate reaches the intermediate value of the maximum value; and executing a control processing that includes controlling, based on the calculated learning rate, an amount of update at the time when a weight parameter is updated in an update processing.

Abstract: Each of a plurality of processors enters, to a model representing a neural network and including a common first weight, first data different from that used by the other processors, calculates an error gradient for the first weight, and integrates the gradients calculated by each processor. Each processor stores the first weight in a memory and updates the weight of the model to a second weight based on a hyperparameter value different from those used by the other processors, the integrated error gradient, and the first weight. Each processor enters common second data to the model, compares the evaluation results acquired by each processor, and selects a common hyperparameter value. Each processor updates the weight of the model to a third weight based on the selected hyperparameter value, the integrated error gradient, and the first weight stored in the memory.