Abstract: A method may include classifying a text by applying a dense convolutional neural network trained to classify the text. The dense convolutional neural network may include one or more dense convolution blocks, each of which including a plurality of convolution layers. Each dense convolution block may be configured to operate on a different quantity of consecutive tokens from the text. Moreover, each of the plurality of convolution layers in a dense convolution block may operate an input to the dense convolution block as well as an output from all preceding convolution layers in the dense convolution block. The text may correspond to an issue associated with a service ticket system. A response for addressing the issue associated with the test may be determined based on the classification of the text. Related systems and articles of manufacture are also provided.

Abstract: Technologies are described for performing adaptive high-resolution digital image processing using neural networks. For example, a number of different regions can be defined representing portions of a digital image. One of the regions covers the entire digital image at a reduced resolution. The other regions cover less than the entire digital image at resolutions higher than the region covering the entire digital image. Neural networks are then used to process each of the regions. The neural networks share information using prolongation and restriction operations. Prolongation operations propagate activations from a neural network operating on a lower resolution region to context zones of a neural network operating on a higher resolution region. Restriction operations propagate activations from the neural network operating on the higher resolution region back to the neural network operating on the lower resolution region.

Abstract: Machine learning models, trained on labeled training data, may be used to categorize documents. To convert data from human-readable text to a form usable by a machine-learning model, a mapping of words to vectors is performed. Learning the mapping to be used is often part of training a machine learning model that operates on text input. A self-supervised pretraining step is performed that aligns the vectors for two or more fields of each document. In this way, when training on the labeled data begins, the vectors used for transforming the text will already be pretrained to give similar values for the two fields. In applications where the two fields are expected to have similar meanings, this pretraining can improve the quality of the resulting model, reduce the amount of training needed, or both.

Abstract: Disclosed herein are system, method, and computer program product embodiments for document information extraction without additional annotations. An embodiment operates by receiving an input representing a document and a key. The embodiment processes the input using a convolutional neural network to obtain a feature map. The embodiment combines the feature map with positional information to obtain a spatial-aware feature map. The embodiment then repeatedly performs the following decoding process: generate attention weights, generate a context vector based on the spatial-aware feature map and the generated attention weights using an attention layer, process the context vector, the key, and an input vector using a recurrent neural network (RNN) to obtain a RNN state, and generate an output vector based on the RNN state and the context vector using a projection layer. The embodiment then extracts a field based on the result of the decoding process.

Abstract: Technologies are described for performing adaptive high-resolution digital image processing using neural networks. For example, a number of different regions can be defined representing portions of a digital image. One of the regions covers the entire digital image at a reduced resolution. The other regions cover less than the entire digital image at resolutions higher than the region covering the entire digital image. Neural networks are then used to process each of the regions. The neural networks share information using prolongation and restriction operations. Prolongation operations propagate activations from a neural network operating on a lower resolution region to context zones of a neural network operating on a higher resolution region. Restriction operations propagate activations from the neural network operating on the higher resolution region back to the neural network operating on the lower resolution region.

Abstract: Technologies are described for the automated determination of semantic overlap between the texts of different classes for use in machine learning. Specifically, a group of classes can be analyzed to determine how much textual overlap and/or semantic overlap is present between the classes before the classes are used for machine learning modeling. If significant overlap is found between the classes, then the classes can be modified before they are used for machine learning modeling.

Abstract: A method may include classifying a text by applying a dense convolutional neural network trained to classify the text. The dense convolutional neural network may include one or more dense convolution blocks, each of which including a plurality of convolution layers. Each dense convolution block may be configured to operate on a different quantity of consecutive tokens from the text. Moreover, each of the plurality of convolution layers in a dense convolution block may operate an input to the dense convolution block as well as an output from all preceding convolution layers in the dense convolution block. The text may correspond to an issue associated with a service ticket system. A response for addressing the issue associated with the test may be determined based on the classification of the text. Related systems and articles of manufacture are also provided.

Abstract: Aspects of the present disclosure therefore involve systems and methods for identifying a set of visually similar scenes to a target scene selected or otherwise identified by a match analyst. A scene retrieval platform performs operations for: receiving an input that comprises an identification of a scene; retrieving a set of coordinates based on the scene identified by the input, where the set of coordinates identify positions of the entities depicted within the frames; generating a set of vector values based on the coordinates of the entities depicted within each of the frames; concatenating the set of vector values to generate a concatenated vector value that represents the scene; generating a visual representation of the concatenated vector value; and identifying one or more similar scenes to the scene identified by the input based on the visual representation of the concatenated vector value.

Abstract: Aspects of the present disclosure therefore involve systems and methods for identifying a set of visually similar scenes to a target scene selected or otherwise identified by a match analyst. A scene retrieval platform performs operations for: receiving an input that comprises an identification of a scene; retrieving a set of coordinates based on the scene identified by the input, where the set of coordinates identify positions of the entities depicted within the frames; generating a set of vector values based on the coordinates of the entities depicted within each of the frames; concatenating the set of vector values to generate a concatenated vector value that represents the scene; generating a visual representation of the concatenated vector value; and identifying one or more similar scenes to the scene identified by the input based on the visual representation of the concatenated vector value.