Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate learning a model for clustered encoding and decoding from a latent probability distribution. A computer implemented method can comprise mapping, by a system operatively coupled to a processor, high-dimensional modalities of data from one or more latent probability distributions corresponding to a plurality of encoder and decoder pairs to a plurality of independent latent spaces. The computer implement method can also comprise mapping, by the system, the plurality of independent latent spaces to a common latent space representing one or more features of one or more input classes associated with the plurality of encoder and decoder pairs.

Abstract: A method, computer program product, and computer system for translating, using a beam search, a source sentence in a source language into a target sentence in a target language by an iterative process.

Abstract: An approach for state-based dynamic multi-dimensional goal reinforcement learning may be provided. The approach may include measuring a reward for an action. The reward may be a dimensional vector of a number of sub-goal dimensions for achieving a final goal. The approach may also include, determining a temporal difference. The temporal difference can be the difference between the reward from an action taken in response to an immediately prior state and the measured reward. The approach may also include updating a Q-table for the action. Updating the Q-table can be based on the first state and the measured reward. Further, the approach may also include predicting a goal for a first state. The prediction can be based on a Q-value from the updated Q-table. Within the approach, the prediction can be based on a deep neural network configured to output a probability.

Abstract: A method, computer program product, and computer system for predicting a class of a given input to multiple models. Class parameters of the models are received. A process that predicts the class of a given input to the models is performed using the class parameters of the models. The process is (i) a model ensemble process predicting that the class of the given input is a class that maximizes a function of class accuracy parameters selected from combinations of the class parameters, (ii) a first threshold process based on a requirement that the class of the given input be predicted by running a model whose class accuracy parameter is at least a specified threshold, or (iii) a second threshold process based on a requirement that a specified class be predicted by a running of all models whose class accuracy parameter is at least the specified threshold.

Abstract: A method, computer program product, and computer system for translating, using a beam search, a source sentence in a source language into a target sentence in a target language by an iterative process.

Abstract: Systems, methods, and computer programming products leveraging cognitive computing and machine learning to dynamically establish circuits within conference calls merging in new callers. The newly established circuits are optimized and established based on location attributes of the new caller and conference call participants, as well as the calling plans and/or costs associated with establishing the circuits to the different conference call participants. New circuits merging in new callers can be prioritized to establish endpoints of the circuit with optimal participants at nearby locations or the same location as the new caller, rather than establishing the endpoints between the new caller and a target participant of the conference call as the default endpoints. Optimized circuits being established between new callers and existing conference call participants can be provisioned entirely within the PSTN of a single service provider or can span across multiple service provider networks.

Abstract: A computer-implemented method includes: receiving, by a computer device, an initial travel plan from a call recipient; identifying, by the computer device, network locations along a route of the initial travel plan, the network locations being geographic locations where a communications network is available to a communication device of the call recipient; and predicting, by the computer device, one or more predicted available time slots of the call recipient, the predicted available time slots being periods of time during which the communication device of the call recipient is predicted to be in one of the network locations along the route, each of the predicted available time slots having a starting time, an ending time, and a duration.