Modeling, Inference and Optimization With Composable Differentiable Procedures

By Dougal Maclaurin. 2016

54 citations recorded by the source corpus.

Subfields: Optimization & Training, Probabilistic & Bayesian Methods

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Citations in the corpus

PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019, methodology): “The autograd [16] package popularized the use of this technique for NumPy arrays, and similar approaches are used in frameworks such as Chainer [5], DyNet [7], Lush [14], Torch [6], Jax [17] and Flux.”
PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018, methodology): “[33] Dougal Maclaurin, Modeling, inference and optimization with composable differentiable procedures, Ph.”
Automatic differentiation in PyTorch (2017, background): “Within this domain, PyTorch’s support for automatic differentiation follows in the steps of Chainer, HIPS autograd [4] and twitter-autograd (twitter-autograd was, itself, a port of HIPS autograd to Lua).”
Compiling machine learning programs via high-level tracing (2018, methodology): “Finally, JAX is fully compatible with Autograd.”
Gradients are Not All You Need (2021, unknown): “…Freeman et al., 2021], graphics [Li et al., 2018, Kato et al., 2020], molecular dynamics [Schoenholz and Cubuk, 2020, Hinsen, 2000], differentiating though optimization procedures [Maclaurin, 2016], weather simulators [Bischof et al., 1996], and nuclear fusion simulators [McGreivy et al., 2021].”
Reparameterization Gradient for Non-differentiable Models (2018, methodology): “Our implementation 2 is written in Python and uses autograd [18], an automatic differentiation package for Python, to automatically compute the gradient term in RepGrad θ for an arbitrary probabilistic model.”