Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.

Abstract: A method, a structure, and a computer system for enabling telemedicine using printed devices. Exemplary embodiments may include receiving a design for a device and printing the device based on the design using a printer. The exemplary embodiments may further include combining the device with a smart device and utilizing the device to collect data during a telemedicine session administered on the smart device.

Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.

Abstract: Techniques regarding autonomously facilitating the selection of one or more transfer models to enhance the performance of one or more machine learning tasks are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can assess a similarity metric between a source data set and a sample data set from a target machine learning task. The computer executable components can also comprise an identification component that can identify a pre-trained neural network model associated with the source data set based on the similarity metric to perform the target machine learning task.

Abstract: Visual recognition of medications involves determining an identity of each dispensed medication in a plurality of different medications and a dosage for each dispensed medication from a picture containing the plurality of different medications. The medications include non-pill medications. Each dispensed medication and the dosage for each dispensed medication is compared to a therapeutic treatment regime containing a plurality of designated medications and a dosage schedule for each designated medication. Previous failures to follow the therapeutic treatment regime are identified. The picture containing the plurality of different medications is analyzed for any counterfeit medications in the dispensed medications.

Abstract: An information processing system, a computer readable storage medium, and a method for comparing training data with test data. The method can include collecting by a processor of a machine learning system, training data having meta-data information used for training the machine learning system, and test data lacking meta-data information. The method can further include training the machine learning system with the training data, extracting components of the machine learning system from analysis of the training data to provide a training data extraction, extracting components of the machine learning system from analysis of the test data to provide a test data extraction, performing at least a low-dimensional comparison of the training data extraction with the test data extraction using a statistical comparison technique, and generating meta-data information for the test data when the low-dimensional comparison meets or exceeds a predetermined threshold.