Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: A controller includes at least one memory, and at least one processor. The at least one processor is configured to acquire speech, recognize the speech, determine whether the speech is uttered in a quiet voice, and control a movable part of a controlled apparatus in accordance with a result of the speech recognition. The at least one processor is configured to control the movable part of the controlled apparatus such that a sound pressure level of a sound generated by the movable part of the controlled apparatus is lower when it is determined that the speech is uttered in the quiet voice than when it is determined that the speech is not uttered in the quiet voice.

Abstract: An inspection apparatus includes one or more memories, and one or more processors. The one or more processors are configured to identify a first item as a target whose level of consumption is to be observed, and control a drive device to make the level of consumption of the first item, identified as the target, observable by an information acquisition device that observes the level of consumption of the target.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: A data augmentation apparatus includes a memory and processing circuitry coupled to the memory. The processing circuitry is configured to input a first data set including first image data and first text data related to the first image data, perform first image processing on the first image data to obtain second image data, edit the first text data based on contents of the first image processing to obtain the edited first text data as second text data, and output an augmented data set including the second image data and the second text data.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: Provided is an input-output system. An input-output system 100 includes an input-output operation part 110, and an input-output model having a character representation space constructed by a large-scale data set. When an input data and a character representation vector are input into the input-output operation part 110, an output data corresponding to the input data reflecting the characteristics corresponding to the character representation vector is output. Further, the input-output system 100 includes a character representation vector calculation part 120 which, when a character data set, which is a small-scale data set, is input, a character representation vector corresponding to the characteristics represented in the character data set is output by the input-output model and the character representation space fixed at the input-output operation part 110.

Abstract: Embodiments relate to a computer implemented information processing system, method and program product for data access. The information processing system includes a data store having a top tier store and at least another tier store with the top tier store including a counter for each entry of a symbol and another tier store including a representative frequency value defined for the another tier store. A sorter is also provided configured to sort the symbol in the top tier store and the another tier stores according to a value generated in the counter for the assessed symbol. The said sorter is also configured to restore entry of the symbol in the top tier store, in response to a symbol having moved from said top tier store to another tier store, by using the representative frequency value defined for said another store to which said symbol was moved.

Abstract: The information processing device according to one embodiment comprises a storage device for storing model information generated through execution of machine learning while employing learning data, the model information including feature information and weighting information associated with the feature information, for each of a plurality of labels; and a display control device for displaying the feature information included in the model information for at least one label among the plurality of labels on a display device, on the basis of the weighting information associated with the feature information.

Abstract: A method, article of manufacture, and system for enabling context surrounding a search result to be displayed succinctly. The method includes searching a document set configured as a frequency ordered suffix tree to obtain a frequency ordered context tree. Applying dynamic programming to the frequency ordered context tree to retrieve a set (C) of context strings (c) having n1 elements of context strings (c). Defining an area covered by a character string (s) in the entire set of context strings C {c1, . . . , cn1} as the product of (1) the number (n2) of context strings (c) having s as a prefix and (2) the length of character string (s). Obtaining a set of character strings (S) that maximizes the sum of areas. In addition, dynamic programming can include a pruning process such that if an upper limit does not reach a maximum value, the search in progress is abandoned.

Abstract: An apparatus and method for calculating a score of matching a sentence with a query pattern having a dependency structure. The apparatus includes: an input unit acquiring an analysis target sentence, a query pattern and an index value indexing how a linguistic unit in the sentence tends to modify another; and a score calculation unit calculating a matching score indexing the degree of matching of the sentence with the query pattern. The matching score is represented by a function having an index value with which a dependency relation included in the query pattern is associated. The score is calculated by attempting association between a substructure of the query pattern and a range in the sentence and by performing recursive calculation in the substructure and the range while storing partial calculation result of the function in a memory area for reuse.

Abstract: A defect predicate expression extraction device. The device extracts, as candidates for predicate expressions representing defects, predicate expressions occurring in the neighborhood of predicate modifying expressions representing suddenness or predicate modifying expressions representing repeatability. The defect predicate expression extraction device further extracts, as predicate expressions representing normality, predicate expressions occurring in the neighborhood of predicate modifying expressions representing normality and extracts predicate expressions representing defects by removing the predicate expressions representing normality from a list of the candidates for predicate expressions representing defects.

Abstract: An apparatus and method for calculating a score of matching a sentence with a query pattern having a dependency structure. The apparatus includes: an input unit acquiring an analysis target sentence, a query pattern and an index value indexing how a linguistic unit in the sentence tends to modify another; and a score calculation unit calculating a matching score indexing the degree of matching of the sentence with the query pattern. The matching score is represented by a function having an index value with which a dependency relation included in the query pattern is associated. The score is calculated by attempting association between a substructure of the query pattern and a range in the sentence and by performing recursive calculation in the substructure and the range while storing partial calculation result of the function in a memory area for reuse.

Abstract: An apparatus and method for calculating a score of matching a sentence with a query pattern having a dependency structure. The apparatus includes: an input unit acquiring an analysis target sentence, a query pattern and an index value indexing how a linguistic unit in the sentence tends to modify another; and a score calculation unit calculating a matching score indexing the degree of matching of the sentence with the query pattern. The matching score is represented by a function having an index value with which a dependency relation included in the query pattern is associated. The score is calculated by attempting association between a substructure of the query pattern and a range in the sentence and by performing recursive calculation in the substructure and the range while storing partial calculation result of the function in a memory area for reuse.