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Found 3 papers, 1 papers with code
Causal disentanglement of multimodal data
Causal representation learning algorithms discover lower-dimensional representations of data that admit a decipherable interpretation of cause and effect; as achieving such interpretable representations is challenging, many causal learning algorithms utilize elements indicating prior information, such as (linear) structural causal models, interventional data, or weak supervision.
Polynomial-Spline Neural Networks with Exact Integrals
Using neural networks to solve variational problems, and other scientific machine learning tasks, has been limited by a lack of consistency and an inability to exactly integrate expressions involving neural network architectures.
PocketNet: A Smaller Neural Network for Medical Image Analysis
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models.
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