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Found 6 papers, 3 papers with code
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity
Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task.
Miutsu: NTU's TaskBot for the Alexa Prize
This paper introduces Miutsu, National Taiwan University's Alexa Prize TaskBot, which is designed to assist users in completing tasks requiring multiple steps and decisions in two different domains -- home improvement and cooking.
Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning
High diagnostic performance was observed across all viewpoints: area under the curves of the ROC to classify >=mild, >=moderate, =severe steatosis grades were 0. 85, 0. 90, and 0. 93, respectively.
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering
Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability.
A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons
By relating the propagation probabilities in the IC model to the transmission rates and recovering rates in the SIR model, we show 2 approaches of social distancing that can lead to a reduction of $R_0$.
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field
Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders.
