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Found 5 papers, 0 papers with code
Importance-based Multimodal Autoencoder
In this paper we propose the IMA (Importance-based Multimodal Autoencoder) model, a scalable model that learns modality importances and robust multimodal representations through a novel cross-covariance based loss function.
Improving Recurrent Neural Network Responsiveness to Acute Clinical Events
This work presents input data perseveration as a method of training and deploying an RNN model to make its predictions more responsive to newly acquired information: input data is replicated during training and deployment.
The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks
Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies.
Affect-LM: A Neural Language Model for Customizable Affective Text Generation
Human verbal communication includes affective messages which are conveyed through use of emotionally colored words.
Learning Representations of Affect from Speech
Experiments on a well-established real-life speech dataset (IEMOCAP) show that the learnt representations are comparable to state of the art feature extractors (such as voice quality features and MFCCs) and are competitive with state-of-the-art approaches at emotion and dimensional affect recognition.
