Abstract: Methods, systems, apparatus, and tangible non-transitory carrier media encoded with one or more computer programs for classifying an input text block into a sequence of one or more classes in a multi-level hierarchical classification taxonomy. In accordance with particular embodiments, a source sequence of inputs corresponding to the input text block is processed, one at a time per time step, with an encoder recurrent neural network (RNN) to generate a respective encoder hidden state for each input, and the respective encoder hidden states are processed, one at a time per time step, with a decoder RNN to produce a sequence of outputs representing a directed classification path in a multi-level hierarchical classification taxonomy for the input text block.

Abstract: Methods, systems, apparatus, and tangible non-transitory carrier media encoded with one or more computer programs for classifying an input text block into a sequence of one or more classes in a multi-level hierarchical classification taxonomy. In accordance with particular embodiments, a source sequence of inputs corresponding to the input text block is processed, one at a time per time step, with an encoder recurrent neural network (RNN) to generate a respective encoder hidden state for each input, and the respective encoder hidden states are processed, one at a time per time step, with a decoder RNN to produce a sequence of outputs representing a directed classification path in a multi-level hierarchical classification taxonomy for the input text block.

Abstract: Techniques for training robust machine learning models for adversarial input data. Training data for a machine learning (ML) model is received. The training data includes a plurality of labels for data elements. First modified training data is generated by modifying one or more of the plurality of labels in the training data using parameterized label smoothing with a first optimization parameter. The ML model is trained using the first modified training data.

Abstract: Techniques for training robust machine learning models for adversarial input data. Training data for a machine learning (ML) model is received. The training data includes a plurality of labels for data elements. First modified training data is generated by modifying one or more of the plurality of labels in the training data using parameterized label smoothing with a first optimization parameter. The ML model is trained using the first modified training data.

Abstract: Methods, systems, apparatus, and tangible non-transitory carrier media encoded with one or more computer programs for classifying an input text block into a sequence of one or more classes in a multi-level hierarchical classification taxonomy. In accordance with particular embodiments, a source sequence of inputs corresponding to the input text block is processed, one at a time per time step, with an encoder recurrent neural network (RNN) to generate a respective encoder hidden state for each input, and the respective encoder hidden states are processed, one at a time per time step, with a decoder RNN to produce a sequence of outputs representing a directed classification path in a multi-level hierarchical classification taxonomy for the input text block.