no code implementations • 16 Nov 2023 • Anna Wong, Shu Ge, Nassim Oufattole, Adam Dejl, Megan Su, Ardavan Saeedi, Li-wei H. Lehman
In this work, we use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model with high predictive power to train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction.
no code implementations • 31 Oct 2023 • Adam Dejl, Hamed Ayoobi, Matthew Williams, Francesca Toni
Feature attribution methods are widely used to explain neural models by determining the influence of individual input features on the models' outputs.
no code implementations • 9 Aug 2023 • Sameer Khanna, Adam Dejl, Kibo Yoon, Quoc Hung Truong, Hanh Duong, Agustina Saenz, Pranav Rajpurkar
We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time.
1 code implementation • 14 Nov 2022 • Adam Dejl, Harsh Deep, Jonathan Fei, Ardavan Saeedi, Li-wei H. Lehman
Models developed using our framework benefit from the full range of RSPN capabilities, including the abilities to model the full distribution of the data, to seamlessly handle latent variables, missing values and categorical data, and to efficiently perform marginal and conditional inference.