TALE-EHR uses a learned polynomial time weight inside transformer attention, plus pre-trained language model embeddings of medical codes, and reports higher predictive accuracy than six EHR baselines on MIMIC-IV and PIC.
Conditioned Source Separation for Music Instrument Performances
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
In music source separation, the number of sources may vary for each piece and some of the sources may belong to the same family of instruments, thus sharing timbral characteristics and making the sources more correlated. This leads to additional challenges in the source separation problem. This paper proposes a source separation method for multiple musical instruments sounding simultaneously and explores how much additional information apart from the audio stream can lift the quality of source separation. We explore conditioning techniques at different levels of a primary source separation network and utilize two extra modalities of data, namely presence or absence of instruments in the mixture, and the corresponding video stream data.
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Time-Aware Attention for Enhanced Electronic Health Records Modeling
TALE-EHR uses a learned polynomial time weight inside transformer attention, plus pre-trained language model embeddings of medical codes, and reports higher predictive accuracy than six EHR baselines on MIMIC-IV and PIC.