DiffSharp: Automatic Differentiation Library

Automatic Differentiation of Algorithms: From Simulation to Optimization

G. Corliss, C. Faure, A. Griewank, L. Hascoët, and U. Naumann · 2002

Earlier work this paper cites.

Perturbation confusion and referential transparency: Correct functional implementation of forward-mode AD

J. M. Siskind and B. A. Pearlmutter · 2005

Earlier work this paper cites.

Leveraging .NET meta-programming components from F#: Integrated queries and interoperable heterogeneous execution

D. Syme · 2006

Earlier work this paper cites.

Backwards differentiation in AD and neural nets: Past links and new opportunities

Original

P. J. Werbos · 2006

Earlier work this paper cites.

Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation

A. Griewank and A. Walther · 2008

Earlier work this paper cites.

Scalable parallel programming with CUDA

J. Nickolls, I. Buck, M. Garland, and K. Skadron · 2008

Earlier work this paper cites.

Reverse-mode AD in a functional framework: Lambda the ultimate backpropagator

B. A. Pearlmutter and J. M. Siskind · 2008

Cited alongside, same era.

Nesting forward-mode AD in a functional framework

J. M. Siskind and B. A. Pearlmutter · 2008

Cited alongside, same era.

Torch7: A MATLAB-like environment for machine learning

R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011

Cited alongside, same era.

Exploitation of structural sparsity in algorithmic differentiation

E. Varnik · 2011

Cited alongside, same era.

Theano: New features and speed improvements

F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio · 2012

Cited alongside, same era.

Who invented the reverse mode of differentiation?

A. Griewank · 2012

Later among the works it cites.

On the efficient computation of sparsity patterns for Hessians

A. Walther · 2012

Later among the works it cites.

AUGEM: Automatically generate high performance dense linear algebra kernels on x86 CPUs

Q. Wang, X. Zhang, Y. Zhang, and Q. Yi · 2013

Later among the works it cites.

Automatic differentiation in machine learning: A survey

Original

A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind · 2015

Closest in time.

Gradient-based hyperparameter optimization through reversible learning

Original

D. Maclaurin, D. Duvenaud, and R. P. Adams · 2015

Closest in time.