Abstract: A system such as a service of a computing resource service provider includes executable code that, if executed by one or more processors, causes the one or more processors to initiate a training of a machine-learning model with a parameter for the training having a first value, the training to determine a set of parameters for the model, calculate output of the training, and change the parameter of the training to have a second value during the training based at least in part on the output. Training parameters may, in some cases, also be referred to as hyperparameters.

Abstract: Systems and methods for determining molecular structures based on atomic-orbital-based features are described. Atomic-orbital-based features can be utilized in combination with machine-learning methods to predict accurate properties, such as quantum mechanical energy, of molecular systems.

Abstract: Systems and methods for learning based control in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training an adaptive controller. The method includes steps for receiving a set of training data that includes several training samples, wherein each training sample includes a state and a true uncertain effect value. The method includes steps for computing an uncertain effect value based on the state, computing a set of one or more losses based on the true uncertain effect value and the computed uncertain effect value, and updating the adaptive controller based on the computed set of losses.

Abstract: A system such as a service of a computing resource service provider includes executable code that, if executed by one or more processors, causes the one or more processors to initiate a training of a machine-learning model with a parameter for the training having a first value, the training to determine a set of parameters for the model, calculate output of the training, and change the parameter of the training to have a second value during the training based at least in part on the output. Training parameters may, in some cases, also be referred to as hyperparameters.

Abstract: Systems and methods provide a selective instrumentation strategy for monitoring the progress of transactions in a distributed computing system. The monitoring of the transactive processing of jobs is considered through a collection of computer operating stages in a distributed system, using limited information. The monitoring is performed by observing log records (or footprints) produced during each stage of processing in the system. The footprints lack unique transaction identifiers resulting in uncertainties in monitoring transaction instances. The processing stages are selective instrumented to reduce monitoring uncertainty under the given constraints such as limited budget for instrumentation cost.

Abstract: Systems and methods provide a selective instrumentation strategy for monitoring the progress of transactions in a distributed computing system. The monitoring of the transactive processing of jobs is considered through a collection of computer operating stages in a distributed system, using limited information. The monitoring is performed by observing log records (or footprints) produced during each stage of processing in the system. The footprints lack unique transaction identifiers resulting in uncertainties in monitoring transaction instances. The processing stages are selective instrumented to reduce monitoring uncertainty under the given constraints such as limited budget for instrumentation cost.