Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: A method, apparatus and system for anomaly detection in a processor based system includes training a deep learning sequence prediction model using observed baseline behavioral sequences of at least one processor behavior of the processor based system, predicting baseline behavioral sequences from the observed baseline behavioral sequences using the sequence prediction model, determining a baseline reconstruction error distribution profile using the baseline behavioral sequences and the predicted baseline behavioral sequences, predicting test behavioral sequences from observed, test behavioral sequences using the sequence prediction model, determining a testing reconstruction error distribution profile using the observed test behavioral sequences and the predicted test behavioral sequences, and comparing the baseline reconstruction error distribution profile to the testing reconstruction error distribution profile to determine if an anomaly exists in a processor behavior of the processor based system.

Abstract: A method, apparatus and system for anomaly detection in a processor based system includes training a deep learning sequence prediction model using observed baseline behavioral sequences of at least one processor behavior of the processor based system, predicting baseline behavioral sequences from the observed baseline behavioral sequences using the sequence prediction model, determining a baseline reconstruction error distribution profile using the baseline behavioral sequences and the predicted baseline behavioral sequences, predicting test behavioral sequences from observed, test behavioral sequences using the sequence prediction model, determining a testing reconstruction error distribution profile using the observed test behavioral sequences and the predicted test behavioral sequences, and comparing the baseline reconstruction error distribution profile to the testing reconstruction error distribution profile to determine if an anomaly exists in a processor behavior of the processor based system.