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2111.00254
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Equinox: neural networks in JAX via callable PyTrees and filtered transformations
30 October 2021
Patrick Kidger
Cristian Garcia
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Papers citing
"Equinox: neural networks in JAX via callable PyTrees and filtered transformations"
50 / 56 papers shown
Title
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Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics
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Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
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Neural Differential Appearance Equations
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Meta-Learning an Evolvable Developmental Encoding
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32
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Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
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Ekaterina Muravleva
39
2
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Scalable physical source-to-field inference with hypernetworks
Berian James
Stefan Pollok
Ignacio Peis
J. Frellsen
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38
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Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
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26
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Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture
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34
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Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference
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28
9
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Glocal Hypergradient Estimation with Koopman Operator
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immrax
\texttt{immrax}
immrax
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