Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a larger neural network from a smaller neural network. In one aspect, a method includes obtaining data specifying an original neural network; generating a larger neural network from the original neural network, wherein the larger neural network has a larger neural network structure including the plurality of original neural network units and a plurality of additional neural network units not in the original neural network structure; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same outputs from the same inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating a larger neural network from a smaller neural network, the method comprising: obtaining data specifying an original neural network, the original neural network being configured to generate neural network outputs from neural network inputs, the original neural network having an original neural network structure comprising a plurality of original neural network units, each original neural network unit having respective parameters, and each of the parameters of each of the original neural network units having a respective original value; generating a larger neural network from the original neural network, the larger neural network having a larger neural network structure comprising: (i) the plurality of original neural network units, and (ii) a plurality of additional neural network units not in the original neural network structure, each additional neural network unit having respective parameters; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values. 2. The method of claim 1, further comprising: training the original neural network to determine the original values of the parameters of the original neural network. 3. The method of claim 2, wherein the original neural network structure comprises a first original neural network layer having a first number of original units, and wherein generating the larger neural network comprises: adding a plurality of additional neural network units to the first original neural network layer to generate a larger neural network layer. 4. The method of claim 3, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the larger neural network layer to the respective original values for the parameters; and for each additional neural network unit in the larger neural network layer: selecting an original neural network unit in the original neural network layer, and initializing the values of the parameters of the additional neural network unit to be the same as the respective original values for the selected original neural network unit. 5. The method of claim 4, wherein selecting an original neural network unit in the larger neural network layer comprises: randomly selecting an original neural network unit from the original neural network units in the original neural network layer. 6. The method of claim 4, wherein: in the original neural network structure, a second original neural network layer is configured to receive as input outputs generated by the first original neural network layer; in the larger neural network structure, the second original neural network layer is configured to receive as input outputs generated by the larger neural network layer; and initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the second original neural network layer so that, for a given neural network input, the second neural network layer generates the same output in both the original neural network structure and the larger neural network structure. 7. The method of claim 6, wherein the original neural network structure comprises a third original neural network layer configured to receive a third original layer input and generate a third original layer output from the third layer input, and wherein generating the larger neural network comprises: replacing the third original neural network layer with a first additional neural network layer having additional neural network units and a second additional neural network layer having additional neural network units, wherein: the first additional neural network layer is configured to receive the third original layer input and generate a first additional layer output from the third original layer input, and the second additional neural network layer is configured to receive the first additional layer output and generate a second additional layer output from the first additional layer output. 8. The method of claim 7, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the additional neural network units in the first additional neural network layer and in the second additional neural network layer so that, for the same neural network input, the second additional layer output is the same as the third original layer output. 9. The method of claim 7, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the additional neural network units in the first additional neural network layer using the respective original values for the parameters of the original neural network units in the third original neural network layer. 10. A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining data specifying an original neural network, the original neural network being configured to generate neural network outputs from neural network inputs, the original neural network having an original neural network structure comprising a plurality of original neural network units, each original neural network unit having respective parameters, and each of the parameters of each of the original neural network units having a respective original value; generating a larger neural network from the original neural network, the larger neural network having a larger neural network structure comprising: (i) the plurality of original neural network units, and (ii) a plurality of additional neural network units not in the original neural network structure, each additional neural network unit having respective parameters; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values. 11. The system of claim 10, further comprising: training the original neural network to determine the original values of the parameters of the original neural network. 12. The system of claim 11, wherein the original neural network structure comprises a first original neural network layer having a first number of original units, and wherein generating the larger neural network comprises: adding a plurality of additional neural network units to the first original neural network layer to generate a larger neural network layer. 13. The system of claim 12, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the larger neural network layer to the respective original values for the parameters; and for each additional neural network unit in the larger neural network layer: selecting an original neural network unit in the original neural network layer, and initializing the values of the parameters of the additional neural network unit to be the same as the respective original values for the selected original neural network unit. 14. The system of claim 13, wherein selecting an original neural network unit in the larger neural network layer comprises: randomly selecting an original neural network unit from the original neural network units in the original neural network layer. 15. The system of claim 13, wherein: in the original neural network structure, a second original neural network layer is configured to receive as input outputs generated by the first original neural network layer; in the larger neural network structure, the second original neural network layer is configured to receive as input outputs generated by the larger neural network layer; and initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the second original neural network layer so that, for a given neural network input, the second neural network layer generates the same output in both the original neural network structure and the larger neural network structure. 16. The system of claim 15, wherein the original neural network structure comprises a third original neural network layer configured to receive a third original layer input and generate a third original layer output from the third layer input, and wherein generating the larger neural network comprises: replacing the third original neural network layer with a first additional neural network layer having additional neural network units and a second additional neural network layer having additional neural network units, wherein: the first additional neural network layer is configured to receive the third original layer input and generate a first additional layer output from the third original layer input, and the second additional neural network layer is configured to receive the first additional layer output and generate a second additional layer output from the first additional layer output. 17. A computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: obtaining data specifying an original neural network, the original neural network being configured to generate neural network outputs from neural network inputs, the original neural network having an original neural network structure comprising a plurality of original neural network units, each original neural network unit having respective parameters, and each of the parameters of each of the original neural network units having a respective original value; generating a larger neural network from the original neural network, the larger neural network having a larger neural network structure comprising: (i) the plurality of original neural network units, and (ii) a plurality of additional neural network units not in the original neural network structure, each additional neural network unit having respective parameters; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values. 18. The computer storage medium of claim 17, further comprising: training the original neural network to determine the original values of the parameters of the original neural network. 19. The computer storage medium of claim 18, wherein the original neural network structure comprises a first original neural network layer having a first number of original units, and wherein generating the larger neural network comprises: adding a plurality of additional neural network units to the first original neural network layer to generate a larger neural network layer. 20. The computer storage medium of claim 19, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the larger neural network layer to the respective original values for the parameters; and for each additional neural network unit in the larger neural network layer: selecting an original neural network unit in the original neural network layer, and initializing the values of the parameters of the additional neural network unit to be the same as the respective original values for the selected original neural network unit.