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Showing 1 to 4 of 4 for “"Geometric Machine Learning"”.
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Topics in Geometric Machine Learning
… adoption of neural networks have revolutionized machine learning and artificial intelligence. These developments demand learning paradigms capable of processing data from diverse applications and sources. In structured domains such as molecules, graphs, sets, and 3D objects, as well as fields …
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Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning
… but is limited by high computational cost. Learning-based force fields have made major progress in accelerating ab-initio MD simulation but are still not fast enough for many real-world applications that require long-time MD simulation. In this paper, we adopt a different machine learning …
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Interpretable Physics-informed Machine Learning Methods for Scientific Modeling and Data Analysis
With the recent advancement of modern machine learning methods, there are now many exciting opportunities to use machine learning in scientific research, including for modeling and data analysis. Machine learning has the potential to become an indispensable tool for scientific discovery, but it is …
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Theoretical Properties of Equivariant Neural Networks
… framework to analyze the expressivity of deep learning architectures through the lens of approximation theory, with a primary focus on symmetry-preserving models. We study equivariant neural networks, where inductive biases can be encoded precisely in representation-theoretic terms by …