An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.

Methods, systems, and devices for automated feature selection and model generation are described. A device (e.g., a server, user device, database, etc.) may perform model generation for an underlying dataset and a specified outcome variable. The device may determine relevance measurements (e.g., stump R-squared values) for a set of identified features of the dataset and can reduce the set of features based on these relevance measurements (e.g., according to a double-box procedure). Using this reduced set of features, the device may perform a least absolute shrinkage and selection operator (LASSO) regression procedure to sort the features. The device may then determine a set of nested linear models—where each successive model of the set includes an additional feature of the sorted features—and may select a “best” linear model for model generation based on this set of models and a model quality criterion (e.g., an Akaike information criterion (AIC)).

Techniques are described for optimizing and assigning video encoding ladders.

Methods and systems are disclosed for generating optimal data predictions in time series data signals based on empirically-optimized model selection, noise filtering, and window size selection using machine learning models. For example, the system may receive a first subset of time series data. The system may receive a prediction horizon. The system may generate a feature input based on the first subset of time series data and the prediction horizon. The system may input the feature input into a machine learning model, wherein the machine learning model includes multiple components. The system may receive an output from the machine learning model. The system may generate for display, on a user interface, a prediction for the first subset of time series data at the prediction horizon based on the output.

A novel and useful system and method of improved power performance and lowered memory requirements for an artificial neural network based on packing memory utilizing several structured sparsity mechanisms. The invention applies to neural network (NN) processing engines adapted to implement mechanisms to search for structured sparsity in weights and activations, resulting in a considerably reduced memory usage. The sparsity guided training mechanism synthesizes and generates structured sparsity weights. A compiler mechanism within a software development kit (SDK), manipulates structured weight domain sparsity to generate a sparse set of static weights for the NN. The structured sparsity static weights are loaded into the NN after compilation and utilized by both the structured weight domain sparsity mechanism and the structured activation domain sparsity mechanism. The application of structured sparsity lowers the span of search options and creates a relatively loose coupling between the data and control planes.

This application provides a method for managing a resource in a computer system and a terminal device. The method includes: obtaining data, where the data includes application sequence feature data related to a current foreground application, and the data further includes at least one of the following real-time data: a system time of the computer system, current status data of the computer system, and current location data of the computer system; selecting, from a plurality of machine learning models based on at least one of the real-time data, a target machine learning model that matches the real-time data; inputting the obtained data into the target machine learning model to rank importance of a plurality of applications installed in the computer system; and performing resource management based on a result of the importance ranking.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for setting ad breakpoints in a video. The program and method provide for accessing a video; determining plural shot boundaries for the video, each shot boundary defining a shot corresponding to a contiguous sequence of video frames that is free of cuts or transitions; and for each shot boundary of the plural shot boundaries, performing a set of breakpoint tests on the shot boundary, each breakpoint test configured to return a respective score indicating whether the shot boundary corresponds to a breakpoint for potential insertion of an ad during playback of the video, calculating a combined score for the shot boundary based on combining the each of the respective scores, and setting, in a case where the combined score meets a threshold value, the shot boundary as the breakpoint.

Aspects of the disclosure relate to using machine learning methods for chatbot selection. A computing platform may train a plurality of machine learning models, each corresponding to a chatbot. The computing platform may train an additional machine learning model to route queries to the plurality of machine learning models based on contents of the queries. The computing platform may receive a query, and may analyze the query using the additional machine learning model. The computing platform may route, based on the query analysis, the query to the plurality of machine learning models. The computing platform may generate, using the plurality of machine learning models, a response to the query. The computing platform may send the response to the query and one or more commands directing a client device to display the response to the query, which may cause the client device to display the response to the query.

A method performed by a computing device includes obtaining a set of image segment identigens for image segments of an image to produce sets of image segment identigens. A set of image segment identigens is a set of possible interpretations of a first image segment of the image segments. The method further includes identifying a subset of valid image segment identigens of each set of image segment identigens by applying identigen rules to the sets of image segment identigens to produce subsets of valid image segment identigens. Each valid image segment identigen of a subset of valid image segment identigens represents a most likely interpretation of a corresponding image segment. The method further includes generating an image entigen group utilizing the subsets of valid image segment identigens, where the image entigen group represents a most likely interpretation of the image.