Hyper-parameters are selected for training a deep convolutional network by selecting a number of network architectures as part of a database. Each of the network architectures includes one or more local logistic regression layer and is trained to generate a corresponding validation error that is stored in the database. A threshold error for identifying a good set of network architectures and a bad set of network architectures may be estimated based on validation errors in the database. The method also includes choosing a next potential hyper-parameter, corresponding to a next network architecture, based on a metric that is a function of the good set of network architectures. The method further includes selecting a network architecture, from among next network architectures, with a lowest validation error.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of selecting hyper-parameters for training a deep convolutional network, comprising: selecting a number of network architectures as part of a database, each of the network architectures including at least one local logistic regression layer; training each of the network architectures to generate a corresponding validation error that is stored in the database; estimating a threshold error for identifying a good set of network architectures and a bad set of network architectures based at least in part on validation errors in the database; choosing a next potential hyper-parameter, corresponding to a next network architecture, based at least in part on a metric that is a function of the good set of network architectures; and selecting a network architecture, from among a plurality of next network architectures, with a lowest validation error. 2. The method of claim 1, in which each of the network architectures includes at least one logistic regression layer. 3. The method of claim 1, further comprising selecting learning hyper-parameters based at least in part on the selected network architecture. 4. The method of claim 3, further comprising: selecting additional network architectures; selecting additional learning hyper-parameters; estimating soft probabilities from the additional network architectures and learning hyper-parameters; and identifying additional hyper-parameters associated with soft probabilities based at least in part on the estimated soft probabilities. 5. The method of claim 1, in which selecting the network architecture comprises jointly selecting architecture hyper-parameters and learning hyper-parameters. 6. The method of claim 1, further comprising choosing the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from the good set of network architectures and evaluating the next potential hyper-parameter based at least in part on a distribution of the good set of network architectures. 7. The method of claim 1, further comprising choosing the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from the good set of network architectures and evaluating the next potential hyper-parameter based at least in part on a ratio of a distribution of the good set of network architectures and a distribution of the bad set of network architectures. 8. The method of claim 1, further comprising choosing the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from a random distribution and evaluating the next potential hyper-parameter based at least in part on a ratio of a distribution of the good set of network architectures and a distribution of the bad set of network architectures. 9. The method of claim 1, further comprising choosing the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from a random distribution and evaluating the next potential hyper-parameter based at least in part on a distribution of the good set of network architectures. 10. The method of claim 1, further comprising choosing the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from either the good set of network architectures or a random distribution, and evaluating the next potential hyper-parameter based at least in part on either a ratio of a distribution of the good set of network architectures and a distribution of the bad set of network architectures or the distribution of the good set of network architectures based at least in part on a probability distribution. 11. An apparatus for selecting hyper-parameters for training a deep convolutional network, comprising: a memory; and at least one processor coupled to the memory and configured: to select a number of network architectures as part of a database, each of the network architectures including at least one local logistic regression layer; to train each of the network architectures to generate a corresponding validation error that is stored in the database; to estimate a threshold error for identifying a good set of network architectures and a bad set of network architectures based at least in part on validation errors in the database; to choose a next potential hyper-parameter, corresponding to a next network architecture, based at least in part on a metric that is a function of the good set of network architectures; and to select a network architecture, from among a plurality of next network architectures, with a lowest validation error. 12. The apparatus of claim 11, in which each of the network architectures includes at least one logistic regression layer. 13. The apparatus of claim 11, in which the at least one processor is further configured to select learning hyper-parameters based at least in part on the selected network architecture. 14. The apparatus of claim 13, in which the at least one processor is further configured: to select additional network architectures; to select additional learning hyper-parameters; to estimate soft probabilities from the additional network architectures and learning hyper-parameters; and to identify additional hyper-parameters associated with soft probabilities based at least in part on the estimated soft probabilities. 15. The apparatus of claim 11, in which the at least one processor is further configured to select the network architecture by jointly selecting architecture hyper-parameters and learning hyper-parameters. 16. The apparatus of claim 11, in which the at least one processor is further configured to choose the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from the good set of network architectures and evaluating the next potential hyper-parameter based at least in part on a distribution of the good set of network architectures. 17. The apparatus of claim 11, in which the at least one processor is further configured to choose the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from the good set of network architectures and evaluating the next potential hyper-parameter based at least in part on a ratio of a distribution of the good set of network architectures and a distribution of the bad set of network architectures. 18. The apparatus of claim 11, in which the at least one processor is further configured to choose the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from a random distribution and evaluating the next potential hyper-parameter based at least in part on a ratio of a distribution of the good set of network architectures and a distribution of the bad set of network architectures. 19. The apparatus of claim 11, in which the at least one processor is further configured to choose the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from a random distribution and evaluating the next potential hyper-parameter based at least in part on a distribution of the good set of network architectures. 20. The apparatus of claim 11, in which the at least one processor is further configured to choose the next potential hyper-parameter by selecting at least one of an architecture hyper-parameter, a learning hyper-parameter, or a soft probability hyper-parameter from either the good set of network architectures or a random distribution, and evaluating the next potential hyper-parameter based at least in part on either a ratio of a distribution of the good set of network architectures and a distribution of the bad set of network architectures or the distribution of the good set of network architectures based at least in part on a probability distribution. 21. An apparatus for selecting hyper-parameters for training a deep convolutional network, comprising: means for selecting a number of network architectures as part of a database, each of the network architectures including at least one local logistic regression layer; means for training each of the network architectures to generate a corresponding validation error that is stored in the database; means for estimating a threshold error for identifying a good set of network architectures and a bad set of network architectures based at least in part on validation errors in the database; means for choosing a next potential hyper-parameter, corresponding to a next network architecture, based at least in part on a metric that is a function of the good set of network architectures; and means for selecting a network architecture, from among a plurality of next network architectures, with a lowest validation error. 22. A non-transitory computer-readable medium having encoded thereon program code for selecting hyper-parameters for training a deep convolutional network, the program code being executed by a processor and comprising: program code to select a number of network architectures as part of a database, each of the network architectures including at least one local logistic regression layer; program code to train each of the network architectures to generate a corresponding validation error that is stored in the database; program code to estimate a threshold error for identifying a good set of network architectures and a bad set of network architectures based at least in part on validation errors in the database; program code to choose a next potential hyper-parameter, corresponding to a next network architecture, based at least in part on a metric that is a function of the good set of network architectures; and program code to select a network architecture, from among a plurality of next network architectures, with a lowest validation error.