Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that provide a platform for on-demand selection of machine-learning models and on-demand learning of parameters for the selected machine-learning models via cloud-based systems. For instance, the disclosed system receives a request indicating a selection of a machine-learning model to perform a machine-learning task (e.g., a natural language task) utilizing a specific dataset (e.g., a user-defined dataset). The disclosed system utilizes a scheduler to monitor available computing devices on cloud-based storage systems for instantiating the selected machine-learning model. Using the indicated dataset at a determined cloud-based computing device, the disclosed system automatically trains the machine-learning model.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that provide a platform for on-demand selection of machine-learning models and on-demand learning of parameters for the selected machine-learning models via cloud-based systems. For instance, the disclosed system receives a request indicating a selection of a machine-learning model to perform a machine-learning task (e.g., a natural language task) utilizing a specific dataset (e.g., a user-defined dataset). The disclosed system utilizes a scheduler to monitor available computing devices on cloud-based storage systems for instantiating the selected machine-learning model. Using the indicated dataset at a determined cloud-based computing device, the disclosed system automatically trains the machine-learning model.

Abstract: In implementations of collecting multimodal image editing requests (IERs), a user interface is generated that exposes an image pair including a first image and a second image including at least one edit to the first image. A user simultaneously speaks a voice command and performs a user gesture that describe an edit of the first image used to generate the second image. The user gesture and the voice command are simultaneously recorded and synchronized with timestamps. The voice command is played back, and the user transcribes their voice command based on the play back, creating an exact transcription of their voice command. Audio samples of the voice command with respective timestamps, coordinates of the user gesture with respective timestamps, and a transcription are packaged as a structured data object for use as training data to train a neural network to recognize multimodal IERs in an image editing application.

Abstract: In implementations of collecting multimodal image editing requests (IERs), a user interface is generated that exposes an image pair including a first image and a second image including at least one edit to the first image. A user simultaneously speaks a voice command and performs a user gesture that describe an edit of the first image used to generate the second image. The user gesture and the voice command are simultaneously recorded and synchronized with timestamps. The voice command is played back, and the user transcribes their voice command based on the play back, creating an exact transcription of their voice command. Audio samples of the voice command with respective timestamps, coordinates of the user gesture with respective timestamps, and a transcription are packaged as a structured data object for use as training data to train a neural network to recognize multimodal IERs in an image editing application.

Abstract: In one embodiment, a device identifies, from image data captured by one or more cameras of a physical location, a focal point of interest and people located within the physical location. The device forms a set of nodes whereby a given node represents one or more of the identified people located within the physical location. The device represents a person queue as an ordered list of nodes from the set of nodes and adds a particular one of the set of nodes to the list based on the particular node being within a predefined distance to the focal point of interest. The device adds one or more nodes to the list based on the added node being within an angle and distance range trailing a forward direction associated with at least one node in the list. The device provides an indication of the person queue to an interface.

Abstract: In one embodiment, a device identifies, from image data captured by one or more cameras of a physical location, a focal point of interest and people located within the physical location. The device forms a set of nodes whereby a given node represents one or more of the identified people located within the physical location. The device represents a person queue as an ordered list of nodes from the set of nodes and adds a particular one of the set of nodes to the list based on the particular node being within a predefined distance to the focal point of interest. The device adds one or more nodes to the list based on the added node being within an angle and distance range trailing a forward direction associated with at least one node in the list. The device provides an indication of the person queue to an interface.