Abstract: A method for optimizing machine learning model generation, the method comprising obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices; determining model accuracy for each of the first machine learning models; selecting models of a predetermined number having highest model accuracy from the first machine learning models; generating second generation indices based on second features from generation indices from the first generation indices associated with the models of the predetermined number; generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment.

Abstract: An information processing system according to the present application is an information processing system including a plurality of nodes including a first node and a second node that execute distributed processing, in which the first node creates a job list including a plurality of jobs into which information processing requested to be processed in the information processing system is divided, and stores the created job list into a common storage accessible to each of the plurality of nodes, and the second node refers to the common storage, determines a targeted job being a job in the job list that is to be set as a processing target, and updates the job list after processing of the targeted job.

Abstract: An information processing method according to the present application is an information processing method executed by a computer, the information processing method including: acquiring learning data used for learning of a model having at least one block to which an output from an input layer is input, the learning data including a plurality of types of information; and selecting a type included in data to be input to the block by processing based on a genetic algorithm in learning using the learning data, and generating the model by using data corresponding to a combination of types selected among the plurality of types as an input from the input layer to the block.

Abstract: An information processing method according to the present application is an information processing method executed by a computer, the information processing method including: acquiring learning data used for learning of a model having a plurality of blocks each including at least one module; and generating the model in which an input to one module is connected as an input to another module by learning using the learning data.

Abstract: An information processing method including: acquiring learning data used for learning of a model having a plurality of blocks including a first block to which an output from a first input layer is input and a second block to which an output from a second input layer different from the first input layer is input, the learning data including a plurality of types of information; and selecting a type included in data input to each of the plurality of blocks in learning using the learning data, and generating the model by using first data in which a combination of types selected among the plurality of types is a first combination as an input from the first input layer to the first block and second data in which a combination of types selected is a second combination as an input from the second input layer to the second block.

Abstract: An information processing method according to the present application is an information processing method executed by a computer, the information processing method including: acquiring learning data used for training of a model including a first partial model and a second partial model; and generating, by using the learning data, the model in a manner in which the first partial model is trained by first dropout based on a first dropout rate and the second partial model is trained by second dropout based on a second dropout rate different from the first dropout rate.

Abstract: An information processing method according to the present application is an information processing method executed by a computer, the information processing method including: acquiring information indicating a dropout rate in training of a model; and generating the model having a size based on the dropout rate.

Abstract: An information processing apparatus according to the application concerned includes an obtaining unit that obtains a dataset of training data to be used for the training of a model; and a generating unit that uses the dataset and generates a model in such a way that there is a decrease in the variability in the weight.

Abstract: Improving accuracy of a model. A learning apparatus according to the present application includes: a dividing unit that divides predetermined learning data features of which are to be learned by a model by training, into a plurality of sets in chronological order; and a training unit that trains the model to learn the features of the learning data included in the set obtained by the division by the dividing unit, for each of the divided sets, in a predetermined order.

Abstract: Improving accuracy of a model. A learning apparatus according to the present application includes: a generation unit that generates a plurality of models each having different parameters; a first training unit that trains each of the plurality of models generated by the generation unit to learn features of a part of predetermined learning data; a selection unit that selects one of the models in accordance with an accuracy of the model trained by the first training unit; and a second training unit that trains the model selected by the selection unit to learn features of predetermined learning data.

Abstract: Improving accuracy of a model. A classification apparatus according to the present application includes: a training unit that trains a model to learn features of learning data having a plurality of attributes; a selection unit that selects a target attribute which is an attribute as non-input target data, that is, which of data having a certain attribute is not to be input to the model, among input candidate data that has a possibility of being input to the model trained by the training unit; and a providing unit that provides information indicating attributes other than the target attribute selected by the selection unit, and the model.

Abstract: An execution control apparatus according to the present application includes: a specifying unit that specifies features of a model used when a plurality of arithmetic units each having different architectures executes a predetermined process; a decision unit that decides an arithmetic unit as an execution target, that is, decides which of the plurality of arithmetic units is to execute the process using the model, based on the features of the model specified by the specifying unit; and an execution control unit that controls the arithmetic unit decided by the decision unit to execute the process using the model.

Abstract: Improving accuracy of a model. A learning apparatus according to the present application includes: a dividing unit that divides predetermined learning data features of which are to be learned by a model by training, into a plurality of sets in chronological order; a selection unit that selects sets to be used for the training of the model, from among the sets obtained by the division by the dividing unit; and a training unit that trains the model to learn the features of the learning data included in each of the sets selected by the selection unit, by using the sets in order from the set in which the learning data included is older in time series, among the sets selected by the selection unit.

Abstract: A learning device according to the present application includes a generation unit that generates, from a plurality of values indicating features of a predetermined target and indicating different types of a plurality of features, a value corresponding to a set of the types of the features, and a learning unit that causes a model to learn a feature of the predetermined target using a value generated by the generation unit.

Abstract: An injection molded shaft includes a shaft body having a first connecting part formed integrally with the first torque applied part, a second connecting part formed integrally with the second torque applied part, a core part extending from the first connecting part to the second connecting part along the longitudinal center axis, the core part having a crisscross cross section orthogonal to the axial direction, and a framework part disposed in a portion partitioned by the first connecting part, the second connecting part, and the core part. The framework part is a diagonal brace extending diagonally across the first connecting part, the second connecting part, and the core part.

Abstract: In a shaft body, a first end in an axial direction of a core part having a crisscross cross section orthogonal to the axial direction is connected to a helical gear via a first connecting part, and the second end in the axial direction of the core part is connected to a worm via a second connecting part. A plurality of first framework parts are formed at regular intervals along a direction of a shaft core in the core part between the first connecting part and the second connecting part. A second framework part extends across the first connecting part, the first framework part, and the core part like a diagonal brace. A third framework part extends across a pair of the first framework parts adjacent to each other and the core part like a diagonal brace. A fourth framework part extends across the second connecting part, the first framework part, and the core part like a diagonal brace.

Abstract: A computer implemented method for generating and optimizing an artificial intelligence model, the method comprising receiving input data and labels, and performing data validation to generate a configuration file, and splitting the data to generate split data for training and evaluation; performing training and evaluation of the split data to determine an error level, and based on the error level, performing an action, wherein the action comprises at least one of modifying the configuration file and tuning the artificial intelligence model automatically; generating the artificial intelligence model based on the training, the evaluation and the tuning; and serving the model for production.

Abstract: A mesh filter includes a circular plate-shaped mesh portion having a plurality of openings allowing a fluid to pass, a ring-shaped frame body supporting an outer periphery of the mesh portion, and a button-shaped thick portion positioned on an inner peripheral side of the mesh portion. The frame body includes a gate detachment part at one place in a circumferential direction. The mesh portion includes a plurality of inner ribs and outer ribs symmetrical with respect to an imaginary straight line passing through a center of the gate detachment part and bisecting a planar shape of the frame body. The four inner ribs are formed at regular intervals in a peripheral area of a center axis of the thick portion. The outer ribs extend radially inward from an inner peripheral surface of the frame body and are separately formed on both sides of extended lines of the respective inner ribs.

Abstract: In a shaft body, a first end in an axial direction of a core part having a crisscross cross section orthogonal to the axial direction is connected to a helical gear via a first connecting part, and the second end in the axial direction of the core part is connected to a worm via a second connecting part. A plurality of first framework parts are formed at regular intervals along a direction of a shaft core in the core part between the first connecting part and the second connecting part. A second framework part extends across the first connecting part, the first framework part, and the core part like a diagonal brace. A third framework part extends across a pair of the first framework parts adjacent to each other and the core part like a diagonal brace. A fourth framework part extends across the second connecting part, the first framework part, and the core part like a diagonal brace.

Abstract: In a shaft body, a first end in an axial direction of a core part having a crisscross cross section orthogonal to the axial direction is connected to a helical gear via a first connecting part, and the second end in the axial direction of the core part is connected to a worm via a second connecting part. A plurality of first framework parts are formed at regular intervals along a direction of a shaft core in the core part between the first connecting part and the second connecting part. A second framework part extends across the first connecting part, the first framework part, and the core part like a diagonal brace. A third framework part extends across a pair of the first framework parts adjacent to each other and the core part like a diagonal brace. A fourth framework part extends across the second connecting part, the first framework part, and the core part like a diagonal brace.