Abstract: Disclosed herein at methods and systems for visualizing machine learning model performance. One method comprises receiving a request to provide a visual representation of a machine learning technique executed on a set of images to generate a first attribute and a second attribute for each image; executing the machine learning model to receive the first and the second attribute for each image; mapping the first attribute to a visual distinctiveness protocol; identifying a distance for each image, the distance representing a difference between the second attribute predicted by the model for each pair of respective images within the set of images; and providing for display at least a subset of the set of images arranged in accordance with their respective distance and having a visual attribute corresponding to the mapped first attribute for each image.

Abstract: Disclosed herein at methods and systems for visualizing machine learning model performance. One method comprises receiving a request to provide a visual representation of a machine learning technique executed on a set of images to generate a first attribute and a second attribute for each image; executing the machine learning model to receive the first and the second attribute for each image; mapping the first attribute to a visual distinctiveness protocol; identifying a distance for each image, the distance representing a difference between the second attribute predicted by the model for each pair of respective images within the set of images; and providing for display at least a subset of the set of images arranged in accordance with their respective distance and having a visual attribute corresponding to the mapped first attribute for each image.

Abstract: This disclosure relates generally to artificial intelligence structured to generate models based on multimodal input. At least one aspect is directed to a system. The system can include a data processing system comprising memory and one or more processors to generate, by a first model trained using machine learning with input including one or more first features each associated with data structures having a plurality of distinct data types, one or more second features compatible with one of the distinct data types, generate, by a second model trained with input including the second features, a plurality of cluster classifications each compatible with one or more of the distinct data types, and cause a user interface to present one or more of the data structures rendered according to a spatial structure based on the second features and the cluster classifications.