Abstract: A deep neural network architecture comprises a stack of strata in which each stratum has its individual input and an individual objective, in addition to being activated from the system input through lower strata in the stack and receiving back propagation training from the system objective back propagated through higher strata in the stack of strata. The individual objective for a stratum may comprise an individualized target objective designed to achieve diversity among the strata. Each stratum may have a stratum support subnetwork with various specialized subnetworks. These specialized subnetworks may comprise a linear subnetwork to facilitate communication across strata and various specialized subnetworks that help encode features in a more compact way, not only to facilitate communication across strata but also to increase interpretability for human users and to facilitate communication with other machine learning systems.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: Computer-based systems and methods add extra terms to the objective function of machine learning systems (e.g., neural networks) in an ensemble for selected items of training data. This selective training is designed to penalize and decrease any tendency for two or more members of the ensemble to make the same mistake on any item of training data, which should result in improved performance of the ensemble in operation.

Abstract: Various systems and methods are described herein for improving the aggressive development of machine learning systems. In machine learning, there is always a trade-off between allowing a machine learning system to learn as much as it can from training data and overfitting on the training data. This trade-off is important because overfitting usually causes performance on new data to be worse. However, various systems and methods can be utilized to separate the process of detailed learning and knowledge acquisition and the process of imposing restrictions and smoothing estimates, thereby allowing machine learning systems to aggressively learn from training data, while mitigating the effects of overfitting on the training data.

Abstract: A machine learning (ML) system includes a student ML system, a learning coach ML system, and a reference system that generates training data for the student ML system. The learning coach ML system learns to make an enhancement to the student ML system or to its learning process, such as updated hyperparameter or a network structural change, based on training of the student ML system with the training data generated by the reference system. The system may also comprise a learning experimentation system that communicates with the reference system to conduct experiments on the learning of the student learning system. Also, the learning experimentation system can determine a cost function for the learning coach ML system.

Abstract: A machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system.

Abstract: Systems and methods to improve the robustness of a network that has been trained to convergence, particularly with respect to small or imperceptible changes to the input data. Various techniques, which can be utilized either individually or in various combinations, can include adding biases to the input nodes of the network, increasing the minibatch size of the training data, adding special nodes to the network that have activations that do not necessarily change with each data example of the training data, splitting the training data based upon the gradient direction, and making other intentionally adversarial changes to the input of the neural network. In more robust networks, a correct classification is less likely to be disturbed by random or even intentionally adversarial changes in the input values.

Abstract: Computer systems and computer-implemented methods recursively train a content-addressable auto-associative memory such that: (i) the content addressable auto-associative memory system is trained to produce an output pattern for each of the input examples; and (ii) a quantity of the learned parameters for the content-addressable auto-associative memory is equal to the number of input variables times a quantity that is independent of the number of input variables. The quantity of learned parameters for the content-addressable auto-associative memory system can be varied based on the number of input examples to be learned.

Abstract: Various systems and methods are described herein for improving the aggressive development of machine learning systems. In machine learning, there is always a trade-off between allowing a machine learning system to learn as much as it can from training data and overfitting on the training data. This trade-off is important because overfitting usually causes performance on new data to be worse. However, various systems and methods can be utilized to separate the process of detailed learning and knowledge acquisition and the process of imposing restrictions and smoothing estimates, thereby allowing machine learning systems to aggressively learn from training data, while mitigating the effects of overfitting on the training data.

Abstract: Computer systems and computer-implemented methods train a machine-learning regression system. The method comprises the step of generating, with a machine-learning generator, output patterns; distorting the output patterns of the generator by a scale factor to generate distorted output patterns; and training the machine-learning regression system to predict the scaling factor, where the regression system receives the distorted output patterns as input and learns and the scaling factor is a target value for the regression system. The method may further comprise, after training the machine-learning regression system, training a second machine-learning generator by back propagating partial derivatives of an error cost function from the regression system to the second machine-learning generator and training the second machine-learning generator using stochastic gradient descent.