A computing device that determines touch interpretation from user input objects, comprising: at least one processor; a machine-learned touch interpretation prediction model, wherein the touch interpretation prediction model has been trained to receive raw touch sensor data indicative of one or more locations of one or more user input objects relative to a touch sensor at one or more times and, in response to receipt of the raw touch sensor data, output simultaneous multiple predicted touch interpretations, each predicted touch interpretation corresponding to a different type of predicted touch interpretation based at least in part on the touch sensor data, each predicted touch interpretation type comprising: a set of touch point interpretations that respectively describe one or more intended touch points, a gesture interpretation that characterizes the set of touch point interpretations as a gesture determined from a predefined gesture class, or a touch prediction vector that describes one or more predicted future locations of the one or more user input objects respectively for one or more future times; and at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising: obtaining a first set of raw touch sensor data indicative of one or more user input object locations relative to a touch sensor over time; inputting the first set of raw touch sensor data into the machine-learned touch interpretation prediction model; receiving, as an output of the touch interpretation prediction model, simultaneous multiple predicted touch interpretations that describe predicted intentions of the one or more user input objects, each predicted touch interpretation corresponding to a different type of predicted touch interpretation; and performing one or more actions associated with the multiple predicted touch interpretations. at least one processor; a machine-learned touch interpretation prediction model, wherein the touch interpretation prediction model has been trained to receive raw touch sensor data indicative of one or more locations of one or more user input objects relative to a touch sensor at one or more times and, in response to receipt of the raw touch sensor data, output simultaneous multiple predicted touch interpretations, each predicted touch interpretation corresponding to a different type of predicted touch interpretation based at least in part on the touch sensor data, each predicted touch interpretation type comprising: a set of touch point interpretations that respectively describe one or more intended touch points, a gesture interpretation that characterizes the set of touch point interpretations as a gesture determined from a predefined gesture class, or a touch prediction vector that describes one or more predicted future locations of the one or more user input objects respectively for one or more future times; and a set of touch point interpretations that respectively describe one or more intended touch points, a gesture interpretation that characterizes the set of touch point interpretations as a gesture determined from a predefined gesture class, or a touch prediction vector that describes one or more predicted future locations of the one or more user input objects respectively for one or more future times; and at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising: obtaining a first set of raw touch sensor data indicative of one or more user input object locations relative to a touch sensor over time; inputting the first set of raw touch sensor data into the machine-learned touch interpretation prediction model; receiving, as an output of the touch interpretation prediction model, simultaneous multiple predicted touch interpretations that describe predicted intentions of the one or more user input objects, each predicted touch interpretation corresponding to a different type of predicted touch interpretation; and performing one or more actions associated with the multiple predicted touch interpretations. obtaining a first set of raw touch sensor data indicative of one or more user input object locations relative to a touch sensor over time; inputting the first set of raw touch sensor data into the machine-learned touch interpretation prediction model; receiving, as an output of the touch interpretation prediction model, simultaneous multiple predicted touch interpretations that describe predicted intentions of the one or more user input objects, each predicted touch interpretation corresponding to a different type of predicted touch interpretation; and