Abstract: Systems, methods, and devices for curating audiovisual content are provided. A mobile image capture device can be operable to capture one or more images; receive an audio signal; analyze at least a portion of the audio signal with a first machine-learned model to determine a first audio classifier label descriptive of an audio event; identify a first image associated with the first audio classifier label; analyze the first image with a second machine-learned model to determine a desirability of a scene depicted by the first image; and determine, based at least in part on the desirability of the scene depicted by the first image, whether to store a copy of the first image associated with the first audio classifier label in the non-volatile memory of the mobile image capture device or to discard the first image without storing a copy of the first image.

Abstract: A HTM network that uses supervision signals such as indexes for correct categories of the input patterns to group the co-occurrences detected in the node. In the training mode, the supervised learning node receives the supervision signals in addition to the indexes or distributions from children nodes. The supervision signal is then used to assign the co-occurrences into groups. The groups include unique groups and nonunique groups. The co-occurrences in the unique group appear only when the input data represent certain category but not others. The nonunique groups include patterns that are shared by one or more categories. In an inference mode, the supervised learning node generates distributions over the groups created in the training mode. A top node of the HTM network generates an output based on the distributions generated by the supervised learning node.

Abstract: A HTM network that uses supervision signals such as indexes for correct categories of the input patterns to group the co-occurrences detected in the node. In the training mode, the supervised learning node receives the supervision signals in addition to the indexes or distributions from children nodes. The supervision signal is then used to assign the co-occurrences into groups. The groups include unique groups and nonunique groups. The co-occurrences in the unique group appear only when the input data represent certain category but not others. The nonunique groups include patterns that are shared by one or more categories. In an inference mode, the supervised learning node generates distributions over the groups created in the training mode. A top node of the HTM network generates an output based on the distributions generated by the supervised learning node.