Preferred Networks, Inc.

Kenta Oono

Ryosuke Okuta

Seiya Tokui

Yuya Unno

Daisuke Okanohara

Nobuyuki Ota

Justin Clayton

Justin B Clayton

A computer system includes one or more memories and one or more processors configured to cause a generative model to generate structural information regarding a chemical compound by inputting a latent representation into the generative model, wherein the generative model has been trained such that differences between structural information regarding other chemical compounds and reconstructions of the structural information regarding the other chemical compounds generated by the generative model are reduced, the reconstructions being generated by inputting latent representations into the generative model, the latent representations being generated based on the structural information regarding the other chemical compounds.

Generative machine learning systems for generating structural information regarding chemical compound

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.

Information processing device and information processing method

A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

Abnormality detection system, abnormality detection method, abnormality detection program, and method for generating learned model

A computer system for generating information regarding chemical compound includes one or more memories and one or more processors configured to generate information regarding chemical compound based on a latent variable, and to evaluate the generated information regarding chemical compound based on desired characteristics, wherein generating the information regarding chemical compound is restricted by the desired characteristics.

Generating information regarding chemical compound based on latent representation

In various embodiments, the systems and methods described herein relate to generative models. The generative models may be trained using machine learning approaches, with training sets comprising chemical compounds and biological or chemical information that relate to the chemical compounds. Deep learning architectures may be used. In various embodiments, the generative models are used to generate chemical compounds that have desired characteristics, e.g. activity against a selected target. The generative models may be used to generate chemical compounds that satisfy multiple requirements.

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Tokyo, Japan

Kenta Oono