Abstract: Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model (108) may be trained for sentence classification, and may combine what is referred to herein as a “residual stacked de-noising autoencoder” (“RSDA”) (220), which may be unsupervised, with a supervised classifier (218) such as a classification neural network (e.g., a multilayer perceptron, or “MLP”). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may not include one or more residual connections. If present, the residual connections may help the RSDA “remember” forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.

Abstract: Methods and systems for generating a question from free text. The system is trained on a corpus of data and receives a tuple consisting of a paragraph (free text), a focused fact, and a question type. The system implements a language model to find the most optimal combination of words to return a question for the paragraph about the focused fact.

Abstract: Techniques disclosed herein related to independent and dependent reading using recurrent networks for natural language inference. In various embodiments, data indicative of a premise (310) and data indicative of a hypothesis (312) form a natural language inference classification pair. For example, the data indicative of a premise can be processed independently using a third recurrent network (318) and data indicative of a hypothesis can be processed independently using a first recurrent network (314). Similarly, data indicative of a premise can be processed dependently using a second recurrent network (316) including data indicative of a hypothesis processed independently. Additionally, data indicative of a hypothesis can be processed dependently using a fourth recurrent network (320) including data indicative of a premise processed independently. Independent and dependent premise data can be pooled (334) together. Independent and dependent hypothesis data can be pooled (336) together.

Abstract: A system for monitoring security in a cyber-physical system comprises: a packet parser configured to obtain, from network traffic in the cyber-physical system, a plurality of sensor measurements from one or more sensors of the cyber-physical system, the plurality of sensor measurements relating to a physical process in the cyber-physical system, the physical process having a current process state; and a threat detector configured to determine, based on a model of the physical process and the current process state, whether the plurality of sensor measurements correspond to a security threat to the cyber-physical system.

Abstract: Methods and systems for generating a question from free text. The system is trained on a corpus of data and receives a tuple consisting of a paragraph (free text), a focused fact, and a question type. The system implements a language model to find the most optimal combination of words to return a question for the paragraph about the focused fact.

Abstract: Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model (108) may be trained for sentence classification and may combine what is referred to herein as a “residual stacked de-noising autoencoder” (“RSDA”) (220), which may be unsupervised, with a supervised classifier (218) such as a classification neural network (e.g., a multilayer perceptron, or “MLP”). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may not include one or more residual connections. If present, the residual connections may help the RSDA “remember” forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.