Google Inc.

Sri International

Zecheng He

Blaise Hilary Aguera-Arcas

Abhinav Kumar Rastogi

Jindong Chen

Xiaoxue Zang

Srinivas Kumar Sunkara

Gabriel Overholt Schubiner

Nevan Holt Wichers

Lijuan Liu

Ying Xu

Ruby B Lee

Sek Meng Chai

Aswin Nadamuni Raghavan

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.).

Machine-learned models for user interface prediction, generation, and interaction understanding

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.

Anomalous behavior detection in processor based systems

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Princeton, NJ, United States of America

Zecheng He