Abstract: Automated data analytics techniques for non-tabular data sets may include methods and systems for (1) automatically developing models that perform tasks in the domains of computer vision, audio processing, speech processing, text processing, or natural language processing; (2) automatically developing models that analyze heterogeneous data sets containing image data and non-image data, and/or heterogeneous data sets containing tabular data and non-tabular data; (3) determining the importance of an image feature with respect to a modeling task, (4) explaining the value of a modeling target based at least in part on an image feature, and (5) detecting drift in image data. In some cases, multi-stage models may be developed, wherein a pre-trained feature extraction model extracts low-, mid-, high-, and/or highest-level features of non-tabular data, and a data analytics models uses those features (or features derived therefrom) to perform a data analytics task.

Abstract: This disclosure relates generally to artificial intelligence structured to generate models based on multimodal input. At least one aspect is directed to a system. The system can include a data processing system comprising memory and one or more processors to generate, by a first model trained using machine learning with input including one or more first features each associated with data structures having a plurality of distinct data types, one or more second features compatible with one of the distinct data types, generate, by a second model trained with input including the second features, a plurality of cluster classifications each compatible with one or more of the distinct data types, and cause a user interface to present one or more of the data structures rendered according to a spatial structure based on the second features and the cluster classifications.

Abstract: Systems and methods are described for developing and using neural network models. An example method of training a neural network includes: oscillating a learning rate while performing a preliminary training of a neural network; determining, based on the preliminary training, a number of training epochs to perform for a subsequent training session, and training the neural network using the determined number of training epochs. The systems and methods can be used to build neural network models that efficiently and accurately handle heterogeneous data.

Abstract: Methods for detection of anomalous data samples from a plurality of data samples are provided. In some embodiments, an anomaly detection procedure that includes a plurality of tasks is executed to identify the anomalous data samples from the plurality of data samples.

Abstract: Systems and methods are described for developing and using neural network models. An example method of training a neural network includes: oscillating a learning rate while performing a preliminary training of a neural network; determining, based on the preliminary training, a number of training epochs to perform for a subsequent training session; and training the neural network using the determined number of training epochs. The systems and methods can be used to build neural network models that efficiently and accurately handle heterogeneous data.

Abstract: Systems and methods are described for developing and using neural network models. An example method of training a neural network includes: oscillating a learning rate while performing a preliminary training of a neural network; determining, based on the preliminary training, a number of training epochs to perform for a subsequent training session; and training the neural network using the determined number of training epochs. The systems and methods can be used to build neural network models that efficiently and accurately handle heterogeneous data.

Abstract: Methods for detection of anomalous data samples from a plurality of data samples are provided. In some embodiments, an anomaly detection procedure that includes a plurality of tasks is executed to identify the anomalous data samples from the plurality of data samples.