Disclosed is a neural network structure enabling efficient training of the network and a method thereto. The structure is a ladder-type structure wherein one or more lateral input(s) is/are taken to decoding functions. By minimizing one or more cost function(s) belonging to the structure the neural network structure may be trained in an efficient way.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-7. (canceled) 8. A trainable neural network structure, comprising a base layer and a second layer, wherein the base layer comprises a corruption function for corrupting an input data of the neural network structure, a decoding function, and a cost function, and the second layer comprises an encoding function, and a decoding function, wherein a corrupted input data is configured to be fed as an input to the encoding function of the second layer, and an encoded corrupted input data is configured to be fed as an input to the decoding function of the second layer and wherein the corrupted input data of the neural network structure and an output of the decoding function of the second layer are configured to be fed as an input to the decoding function of the base layer, wherein the corrupted input data is brought as a lateral input to the decoding function, and wherein an output of the decoding function of the base layer and the input data of the neural network are configured to be fed as an input to the cost function of the base layer. 9. The neural network structure according to the claim 8, wherein the second layer comprises further a cost function, wherein an output of the decoding function for the second layer and the input data of the neural network structure encoded with the encoding function of the second layer are configured to be fed as an input to the cost function. 10. The neural network structure according to claim 8, the neural network structure further comprising: at least one further second layer, wherein each of the at least one further second layer is arranged to be connected to a previous second layer, and wherein an output of the encoding function of the previous second layer is configured to be fed as an input to the encoding function of the at least one further second layer, and an output of the encoding function of the at least one further second layer and an output of the decoding function of an upper layer are configured to be fed as an input to the decoding function in the at least one further second layer. 11. The neural network structure according to claim 10, wherein the at least one further second layer further comprising a cost function, wherein an output of the decoding function of the same at least one further second layer and the input data of the neural network structure encoded with encoding functions of each layer previous to the at least one further second layer in question are configured to be fed as an input to the at least one cost function of the at least one further second layer. 12. A method for training the neural network structure according to claim 8 relating to the neural network structure, the method comprising adjusting at least one parameter of at least one function in at least one of the layers so that a cost function defined for the neural network structure is minimized, when the neural network is trained by inputting input data to the structure. 13. The method of claim 12, wherein the input data of the layer-specific cost function is compared. 14. The method of claims 12, wherein the cost function defined for the structure is a sum of layer-specific cost functions defined for at least one layer in the neural network structure. 15. The neural network structure according to claim 9, the neural network structure further comprising: at least one further second layer, wherein each of the at least one further second layer is arranged to be connected to a previous second layer, and wherein an output of the encoding function of the previous second layer is configured to be fed as an input to the encoding function of the at least one further second layer, and an output of the encoding function of the at least one further second layer and an output of the decoding function of an upper layer are configured to be fed as an input to the decoding function in the at least one further second layer. 16. A method for training the neural network structure according to claim 9 relating to the neural network structure, the method comprising adjusting at least one parameter of at least one function in at least one of the layers so that a cost function defined for the neural network structure is minimized, when the neural network is trained by inputting input data to the structure. 17. A method for training the neural network structure according to claim 10 relating to the neural network structure, the method comprising adjusting at least one parameter of at least one function in at least one of the layers so that a cost function defined for the neural network structure is minimized, when the neural network is trained by inputting input data to the structure. 18. A method for training the neural network structure according to claim 11 relating to the neural network structure, the method comprising adjusting at least one parameter of at least one function in at least one of the layers so that a cost function defined for the neural network structure is minimized, when the neural network is trained by inputting input data to the structure. 19. A method for training the neural network structure according to claim 15 relating to the neural network structure, the method comprising adjusting at least one parameter of at least one function in at least one of the layers so that a cost function defined for the neural network structure is minimized, when the neural network is trained by inputting input data to the structure. 20. The method of claims 13, wherein the cost function defined for the structure is a sum of layer-specific cost functions defined for at least one layer in the neural network structure. 21. The method of claims 16, wherein the cost function defined for the structure is a sum of layer-specific cost functions defined for at least one layer in the neural network structure. 22. The method of claims 17, wherein the cost function defined for the structure is a sum of layer-specific cost functions defined for at least one layer in the neural network structure. 23. The method of claims 18, wherein the cost function defined for the structure is a sum of layer-specific cost functions defined for at least one layer in the neural network structure. 24. The method of claims 19, wherein the cost function defined for the structure is a sum of layer-specific cost functions defined for at least one layer in the neural network structure.