These snakes can go for months without eating, grow and shrink the size of their hearts and jump start their metabolism on a ...
Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...
Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to ...
PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for ...
We demonstrate that a neural network automatically solves, explains, and generates university-level problems from the largest Massachusetts Institute of Technology (MIT) mathematics courses at a human ...
Matrix completion is a fundamental problem in machine learning that arises in various applications. We envision that our infinite width neural network framework for matrix completion will be easily ...
Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We ...
Despite the importance of programming to modern society, the cognitive and neural bases of code comprehension are largely unknown. Programming languages might ‘recycle’ neurocognitive mechanisms ...
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