Abstract: The invention relates to a method for operating a gas heater, in particular a gas heater for providing central heating and/or for providing domestic hot water, in particular a gas boiler, in particular a condensing boiler, wherein the method comprises the following steps: supplying a gas and a fuel gas into a mixing chamber of the gas heater and supplying a mixed gas from the mixing chamber to a burner of the gas heater, measure the differential pressure per unit time and/or provide a sensor signal to the control unit for determining a gas quantity, defined by a volume per unit time or a mass per unit time, before the gas is supplied to the mixing chamber, measure the differential pressure per unit time and/or provide a sensor signal to the control unit for determining a fuel gas quantity, defined by a volume per unit time or a mass per unit time, before the fuel gas is supplied to the mixing chamber and determining whether at least one operation condition of the gas heater is fulfilled based on the determine

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing object sub-types in images. One of the methods includes receiving training data; selecting training data for an image; determining whether to randomly permute a value of a property of the selected image; providing, to a deep neural network, the particular training data or the randomly permuted particular training data; receiving, from the deep neural network, output data indicating a predicted label for an object sub-type for an object depicted in the selected image, and a confidence score that represents a likelihood that the object has the object sub-type; updating one or more weights in the deep neural network using an expected output value, the predicted label, and the confidence score; and providing the deep neural network to a mobile device for use detecting whether one or more images depict objects having the particular object sub-type.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing object sub-types in images. One of the methods includes receiving training data; selecting training data for an image; determining whether to randomly permute a value of a property of the selected image; providing, to a deep neural network, the particular training data or the randomly permuted particular training data; receiving, from the deep neural network, output data indicating a predicted label for an object sub-type for an object depicted in the selected image, and a confidence score that represents a likelihood that the object has the object sub-type; updating one or more weights in the deep neural network using an expected output value, the predicted label, and the confidence score; and providing the deep neural network to a mobile device for use detecting whether one or more images depict objects having the particular object sub-type.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing object sub-types in images. One of the methods includes receiving training data; selecting training data for an image; determining whether to randomly permute a value of a property of the selected image; providing, to a deep neural network, the particular training data or the randomly permuted particular training data; receiving, from the deep neural network, output data indicating a predicted label for an object sub-type for an object depicted in the selected image, and a confidence score that represents a likelihood that the object has the object sub-type; updating one or more weights in the deep neural network using an expected output value, the predicted label, and the confidence score; and providing the deep neural network to a mobile device for use detecting whether one or more images depict objects having the particular object sub-type.