Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised time-series feature learning. The method generates a set of reference time-series of random lengths, in which each length is uniformly sampled from a predetermined minimum length to a predetermined maximum length, and in which values of each reference time-series in the set are drawn from a distribution. The method generates a feature matrix for raw time-series data based on a set of computed distances between the generated set of reference time-series and the raw time-series data. The method provides the feature matrix as an input to one or more machine learning models.

Abstract: A mechanism computes a discounted health variable with a penalty for deviating from clinical guidelines based on a distance function representing an allowed deviation from the clinical guidelines, applies reinforcement learning techniques on the discounted health variable to generate a reinforcement learning (RL) model for generating dynamic treatment regimes, and determines, for a patient for a plurality of times, a next action in a treatment regime using the RL model with no distance function, an optimal next action in the treatment regime with allowed deviation from the guidelines, and a next action in the treatment regime that adheres to the guidelines. The mechanism generates an outcome output display based on the determined next action in a treatment regime using the RL model with no distance function, optimal next action in the treatment regime with allowed deviation from the guidelines, and next action in the treatment regime that adheres to the guidelines.

Abstract: Mechanisms are provided for executing a trained deep learning (DL) model. The mechanisms receive, from a trained autoencoder executing on a client computing device, one or more intermediate representation (IR) data structures corresponding to training input data input to the trained autoencoder. The mechanisms train the DL model to generate a correct output based on the IR data structures from the trained autoencoder, to thereby generate a trained DL model. The mechanisms receive, from the trained autoencoder executing on the client computing device, a new IR data structure corresponding to new input data input to the trained autoencoder. The mechanisms input the new IR data structure to the trained DL model executing on the deep learning service computing system, to generate output results for the new IR data structure. The mechanisms generate an output response based on the output results, which is transmitted to the client computing device.

Abstract: A method involves receiving account registrations and identifying a group of account registrations where each account registration in the group of account registrations shares attributes. The method further involves identifying features of the group of account registrations, and based on the features, determining whether to block a set of accounts that is associated with the group of account registrations.

Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised time-series feature learning. The method generates a set of reference time-series of random lengths, in which each length is uniformly sampled from a predetermined minimum length to a predetermined maximum length, and in which values of each reference time-series in the set are drawn from a distribution. The method generates a feature matrix for raw time-series data based on a set of computed distances between the generated set of reference time-series and the raw time-series data. The method provides the feature matrix as an input to one or more machine learning models.