Abstract: Systems, devices, and methods are provided for detecting and correcting eating behavior. A device may receive audio data, determine that the audio data is indicative of consumption of a product by a user. The device may determine, based on the product, a measureable attribute associated with the user. The device may receive first data associated with the measureable attribute. The device may determine that the first data exceeds a threshold. The device may generate a message for presentation.

Abstract: A machine learning system builds and uses control policies for controlling robotic performance of a task. Such control policies may be trained using targeted updates. For example, two trials identified as similar may be compared and evaluated to determine which trial achieved a greater degree of task success; a control policy update may then be generated based on identified differences between the two trials.

Abstract: Disclosed herein are methods for selecting one or more tumor-specific neoantigens from a tumor of a subject for a personalized immunogenic composition. Also disclosed herein are methods for treating cancer in a subject in need thereof by administering an immunogenic composition comprising tumor-specific neoantigens selected using the methods disclosed herein.

Abstract: A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.

Abstract: A machine learning system builds and uses control policies for controlling robotic performance of a task. Such control policies may be trained using targeted updates, for example by comparing two trials to identify which represents a greater degree of task success, using this to generate updates from a reinforcement learning system, and weighting the updates based on differences between action vectors of the trials.

Abstract: A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.

Abstract: A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.

Abstract: A machine learning system builds and uses computer models for identifying how to evaluate the level of success reflected in a recorded observation of a task. Such computer models may be used to generate a policy for controlling a robotic system performing the task. The computer models can also be used to evaluate robotic task performance and provide feedback for recalibrating the robotic control policy.

Abstract: A machine learning system builds and uses computer models for identifying how to evaluate the level of success reflected in a recorded observation of a task. Such computer models may be used to generate a policy for controlling a robotic system performing the task. The computer models can also be used to evaluate robotic task performance and provide feedback for recalibrating the robotic control policy.