Abstract: There is provided a neural network system for detection of malicious code, the neural network system comprising: an input receiver configured for receiving input text from one or more code input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are malicious code or benign code.

Abstract: Systems and methods of automatically generating a coherence score for text data is provided. The approach includes receiving a plurality of string tokens representing decomposed portions of the target text data object. A trained neural network is provided that has been trained against a plurality of corpuses of training text across a plurality of topics. The string tokens are arranged to extract string tokens representing adjacent sentence pairs of the target text data object. For each adjacent sentence pair, the neural network generates a local coherence score representing a coherence level of the adjacent sentence pair of the target text data object, which are then aggregated for each adjacent sentence pair of the target text data object to generate a global coherence score for the target text data object.

Abstract: An improved computer implemented method and corresponding systems and computer readable media for improving performance of a deep neural network are provided to mitigate effects related to catastrophic forgetting in neural network learning. In an embodiment, the method includes storing, in memory, logits of a set of samples from a previous set of tasks (D1); and maintaining classification information from the previous set of tasks by utilizing the logits for matching during training on a new set of tasks (D2).

Abstract: Systems, methods, and computer readable media are described to train a compressed neural network with high robustness. The neural network is first adversarially pre-trained with both original data as well as data perturbed by adversarial attacks for some epochs, then “unimportant” weights or filters are pruned through criteria based on their magnitudes or other method (e.g., Taylor approximation of the loss function), and the pruned neural network is retrained with both clean and perturbed data for more epochs.

Abstract: A platform, device and process is provided for visual construction of operations for data querying. In particular, embodiments described herein provides a platform, device and process for visual construction of nested operations for data querying. The visual construction is a display of one or more projected data spaces enabling a selection of data indicators on the display. The selection is conducted graphically on the visual construction and the system is configured to translate the selection to generate and conduct a query operating visually on the visualized (e.g., projected) data space. The visual data space includes distinct views of the plurality of multi-dimensionality data points mapped to reduced-dimensionality data points with a transformation function associated with each view. The selections are used to augment the multi-dimensionality data points with one or more additional dimensions to track the selections and to perform operations and visualizations.

Abstract: A method for acquiring measurements for a data structure corresponding to an array of variable includes: selecting a subset of elements from the data structure; measuring a sampled value for each of the selected subset of elements; storing each of the sampled values in a K-nearest neighbour (KNN) database and labelling the sampled value as certain; generating a predicted value data structure where each predicted element is generated as the value of its nearest neighbor based on the values stored in the KNN database; for each predicted element: retrieve the predicted element's X nearest neighbours for the sampled value in the KNN database, and when a value of the X nearest neighbours is the same as the predicted element, the predicted element is labelled as certain, otherwise the predicted element is labelled the values as uncertain; and repeating until all elements are labelled as certain.

Abstract: A system, electronic device and method for improved neural network training are provided. The electronic device includes: a processor, a memory storing a Generative adversarial network (GAN) to learn from unlabeled data by engaging a generative model in an adversarial game with a discriminator; and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for training the Generative adversarial network using a regularizer to encourage the discriminator to properly use its capacity and hidden representations of the discriminator to have high entropy.

Abstract: An electronic device for neural network training includes at least one processor and one or more memories configured to provide or train: a generative adversarial network (GAN) using a generator and a discriminator for: receiving a plurality of training cases; and training the generative adversarial network, based on the plurality of training cases, to classify the training cases; wherein the generator generates hard negative examples for the discriminator.

Abstract: Systems and methods for computationally generating a set of more “stable” configuration default values that are used for traceability and improving reproducibility of machine learning approaches. Hash values are generated based on a merged/modified configuration and both configuration content and hash are stored together in one or more data structures. These data structures can be used to link back to the actual values used in experiments.

Abstract: An interactive data visualization system is provided that utilizes unsupervised learning process, to automatically choose the hyperparameters for generating insights, which are then used for visualizing the data using interactive plots that update dynamically in response to input control commands.

Abstract: There is provided a neural network system for detection of malicious code, the neural network system comprising: an input receiver configured for receiving input text from one or more code input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are malicious code or benign code.

Abstract: There is provided a neural network system for detection of domain generation algorithm generated domain names, the neural network system comprising: an input receiver configured for receiving domain names from one or more input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are DGA-generated or benign domain names.