Abstract: A system, method, and computer program product for hierarchical categorization of sound comprising one or more neural networks implemented on one or more processors. The one or more neural networks are configured to categorize a sound into a two or more tiered hierarchical coarse categorization and a finest level categorization in the hierarchy. The categorization sound may be used to search a database for similar or contextually related sounds.

Abstract: An example operation may include one or more of storing a hierarchical data set, receiving a plurality of predicted outputs from a plurality of nodes in a distributed computing environment, respectively, wherein each predicted output is generated by a different node via execution of a time-series forecasting model on a different subset of lowest level data in the hierarchical data set, combining the plurality of predicted outputs via bottom-up aggregation to generate one or more additional predicted outputs for the time-series forecasting model based on one or more levels above the lowest level in the hierarchical time-series data set, determining error values for the time-series forecasting model at each level of the hierarchical data set based on the received and the one or more additional generated predicted outputs, and modifying a parameter of the time-series forecasting model based on the determined error values.

Abstract: Deriving insights from time series data can include receiving subject matter expert (SME) input characterizing one or more aspects of a time series. A model template that specifies one or more components of the time series can be generated by translating the SME input using a rule-based translator. A machine learning model based on the model template can be a multilayer neural network having one or more component definition layers, each configured to extract one of the one or more components from time series data input corresponding to an instantiation of the time series. With respect to a decision generated by the machine learning model based on the time series data input, a component-wise contribution of each of the one or more components to the decision can be determined. An output can be generated, the output including the component-wise contribution of at least one of the one or more components.

Abstract: An example operation may include one or more of storing a hierarchical time-series data set in memory, initially training a first time-series forecasting model based on a lower level of time-series data in the hierarchical data set, training a second time-series teaching forecasting model based on an upper level of time-series data from the hierarchical data set which includes an additional level of aggregation with respect to the lower level of time-series data, optimizing one or more parameters of the initially trained first time-series forecasting model based on predicted outputs from the trained second time-series forecasting model in comparison to predicted outputs from the initially trained first time-series forecasting model, and storing the modified first time-series forecasting model in the memory.

Abstract: A method, system, and computer program product for an interpretable, feature-based post-hoc black box explainer for univariate time series forecasters are provided. The method receives a set of time series forecasting predictions. The set of time series forecasting predictions are generated from a set of black-box models trained with an initial data set. The method generates a set of features based on at least a portion of the initial data set. A set of surrogate models are trained based on the set of time series forecasting predictions and at least a portion of the set of features. A subset of surrogate models is selected. Based on the subset of surrogate models, the method generates one or more explanation outputs for time series forecasting predictions of the set of black-box models.

Abstract: A system enhances existing audio-visual content with an action a scene annotation module, an action description module, both of which are coupled to a controller. The scene annotation module classifies scene elements from an image frame received from a host system and generates a caption describing the scene elements. The scene annotation module includes a first neural network configured to generate a feature vector from the image frame and a second neural network configured to generate a caption describing elements within the image frame from the feature vector. The action description module recognizes action happening within one or more image frames received from the host system and generates a description of the action happening within one or more image frames.

Abstract: A system enhances existing audio-visual content with audio describing the setting of the visual content. A scene annotation module classifies scene elements from an image frame received from a host system and generates a caption describing the scene elements.

Abstract: Graphical style modification may be implemented using machine learning. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. A Graphical Style Modification module generates a style adapted video stream by applying a style adapted from a target image frame to each image frame in a buffered video stream.

Abstract: Accommodation for color or visual impairments may be implemented by selective color substitution. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. The color accommodation module may include a rule based filter that substitutes one or more colors within the image frame with one or more corresponding alternative colors.

Abstract: Graphical style modification may be implemented using machine learning. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. A Graphical Style Modification module generates a style adapted video stream by applying a style adapted from a target image frame to each image frame in a buffered video stream.

Abstract: Graphical style modification may be implemented using machine learning. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. A Graphical Style Modification module receives a first image frame from a host system and applies a style adapted from a second image frame to the first image frame to generate a style adapted first image frame.

Abstract: Accommodation for color or visual impairments may be implemented by selective color substitution. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. The color accommodation module may include a rule based filter that substitutes one or more colors within the image frame with one or more corresponding alternative colors.

Abstract: A system enhances existing audio visual content with audio describing the action and setting of the visual content. The system may also provide subtitle content describing the important sound or sounds occurring within audio. Accommodation for color or visual impairments may be implemented by selective color substitution. A Graphical Style Modification module may apply a style from one image to another to adapt the style of a video per a gamer's preference.

Abstract: Accommodation for color or visual impairments may be implemented by selective color substitution. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. The color accommodation module may include a rule based filter that substitutes one or more colors within the image frame with one or more corresponding alternative colors.

Abstract: Graphical style modification may be implemented using machine learning. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. A Graphical Style Modification module receives a first image frame from a host system and applies a style adapted from a second image frame to the first image frame to generate a style adapted first image frame.

Abstract: Accommodation for color or visual impairments may be implemented by selective color substitution. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. The color accommodation module may include a rule based filter that substitutes one or more colors within the image frame with one or more corresponding alternative colors.

Abstract: A system enhances existing audio-visual content with audio describing the setting of the visual content. A scene annotation module classifies scene elements from an image frame received from a host system and generates a caption describing the scene elements.

Abstract: A system method and computer program product for hierarchical categorization of sound comprising one or more neural networks implemented on one or more processors. The one or more neural networks are configured to categorize a sound into a two or more tiered hierarchical course categorization and a finest level categorization in the hierarchy. The categorization of a sound may be used to search a database for similar or contextually related sounds.