Abstract: A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.

Abstract: A model management system performs error analysis on results predicted by a machine learning model. The model management system identifies an incorrectly classified image outputted from a machine learning model and identifies using the Neural Template Matching (NTM) algorithm, an additional image correlated to the selected image. The system outputs correlated images based on a given image and a selection by a user through a user interface of a region of interest (ROI) of the given image. The region is defined by a bounding polygon input and the correlated images include features correlated to the features within the ROI. The system prompts a task associated with the additional image. The system receives a response that includes an indication that the additional image is incorrectly labeled and including a replacement label and instruct that the machine learning model be retrained using an updated training dataset that includes the replacement label.

Abstract: A model management system performs error analysis on results predicted by a machine learning model. The model management system identifies an incorrectly classified image outputted from a machine learning model and identifies using the Neural Template Matching (NTM) algorithm, an additional image correlated to the selected image. The system outputs correlated images based on a given image and a selection by a user through a user interface of a region of interest (ROI) of the given image. The region is defined by a bounding polygon input and the correlated images include features correlated to the features within the ROI. The system prompts a task associated with the additional image. The system receives a response that includes an indication that the additional image is incorrectly labeled and including a replacement label and instruct that the machine learning model be retrained using an updated training dataset that includes the replacement label.

Abstract: Security can be improved in a business application or system, such as a mission-critical application, by automatically analyzing user access (UA) and segregation of duties (SoD). This analysis may be using a graphical representation of a model with nodes for business application concepts and edges for relationships between nodes. A review of the graphical representation is used for UA and SoD.

Abstract: A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.

Abstract: In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact.

Abstract: In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact.

Abstract: In various embodiments, a training application trains a classifier to detect executional artifacts in experiments involving microwell plates. The training application computes spatial information based on a heat map associated with a microwell plate. The training application then computes a set of features based on the spatial information. Subsequently, the training application executes one or more machine learning operations based, at least in part, on the set of features to generate a trained classifier. The trained classifier classifies sets of features associated with different microwell plates with respect to labels associated with executional artifacts. Advantageously, the trained classier can be used to accurately and consistently detect executional artifacts across different experiments and over time.

Abstract: Systems, methods, and software are disclosed for facilitating micro documentation environments. In at least one implementation, a micro documentation environment includes subject entities within a micro blogging environment. At least one subject entity corresponds to at least one software component within a software development environment. The micro documentation environment also includes other entities within the micro blogging environment. At least one other entity follows the one subject entity corresponding to the software component. The micro documentation environment also includes micro posts, at least one of which is generated on behalf of the one subject entity and comprises documentation information related to the one software component.