Abstract: An encoder and decoder for translating sequential data into a fixed dimensional vector are created by applying an encoding-trainer input vector set as input to an encoding neural network to generate an encoding-trainer output vector set. One vector of the encoding-trainer output vector set is selected and a decoding-trainer input vector set is generated from it. A decoding neural network is trained by applying the generated decoding-trainer input vector set to the decoding neural network. The encoder and decoder can be used in implementations processing sequential data.

Abstract: A system and method for implementing a machine learning system using dynamic multilayer perceptrons transforms input data into directed acyclic graphs. Dynamic multilayer perceptron network graphs are generated based at least in part on the directed acyclic graphs. During training of the machine learning system, a trained weight set is determined by transforming training data into directed acyclic graphs and dynamic multilayer perceptron network graphs and adjusting the weights of the dynamic multilayer perceptron network graphs. When using the machine learning system, an output value is determined by transforming subject data into a subject dynamic acyclic graph. The trained weight set and subject dynamic acyclic graph are applied to a dynamic multilayer perceptron network graph that was generated based on the subject data resulting in the output.

Abstract: An encoder and decoder for translating sequential data into a fixed dimensional vector are created by applying an encoding-trainer input vector set as input to an encoding neural network to generate an encoding-trainer output vector set. One vector of the encoding-trainer output vector set is selected and a decoding-trainer input vector set is generated from it. A decoding neural network is trained by applying the generated decoding-trainer input vector set to the decoding neural network. The encoder and decoder can be used in implementations processing sequential data.

Abstract: A deep learning source code analyzer and repairer trains neural networks and applies them to source code to detect defects in the source code. The deep learning source code analyzer and repairer can also use neural networks to suggest modifications to source code to repair defects in the source code. The neural networks can be trained using versions of source code with potential defects and accepted modifications addressing the potential defects.