DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing | Paper Citation Record

Observation 7fb51daf-a286-451e-87ea-892a392707f0 · outbound

This paper cites Naumann, The Art of Differentiating Computer Programs.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Naumann, The Art of Differentiating Computer Programs

Reference 1

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This paper cites A review of automatic differentiation and its efficient implementation,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing A review of automatic differentiation and its efficient implementation,

Reference 2

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This paper cites Learning representations by back-propagating errors,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Learning representations by back-propagating errors,

Reference 3

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This paper cites 30 years of adaptive neural networks: perceptron, madaline, and backpropagation,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing 30 years of adaptive neural networks: perceptron, madaline, and backpropagation,

Reference 4

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This paper cites Attention Is All You Need.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Attention Is All You Need

Reference 6

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This paper cites Identification and review of sensitivity analysis methods,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Identification and review of sensitivity analysis methods,

Reference 7

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DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Unresolved cited work

Reference 8

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This paper cites Data assimilation concepts and methods march 1999,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Data assimilation concepts and methods march 1999,

Reference 9

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Observation 808fa792-b39c-4ae1-8727-4d896af7b939 · outbound

This paper cites Neural General Circulation Models for Weather and Climate.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Neural General Circulation Models for Weather and Climate

Reference 10

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This paper cites Advances in weather prediction,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Advances in weather prediction,

Reference 11

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This paper cites Adifor 2.0: automatic differentiation of fortran 77 programs,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Adifor 2.0: automatic differentiation of fortran 77 programs,

Reference 12

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Observation 7e16b619-1690-4ca6-868f-a49bb541a828 · outbound

This paper cites Compiling machine learning programs via high-level tracing,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Compiling machine learning programs via high-level tracing,

Reference 13

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Observation 0f23ed03-a921-4b74-9c1f-0995dc8dac14 · outbound

This paper cites A Differentiable Programming System to Bridge Machine Learning and Scientific Computing.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing A Differentiable Programming System to Bridge Machine Learning and Scientific Computing

Reference 14

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This paper cites Instead of rewriting foreign code for ma- chine learning, automatically synthesize fast gradients,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Instead of rewriting foreign code for ma- chine learning, automatically synthesize fast gradients,

Reference 15

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Observation 4404c9cd-2d5b-4b03-b5d9-b509b480c4ec · outbound

This paper cites Automatic differentiation in pytorch,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Automatic differentiation in pytorch,

Reference 16

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This paper cites Griewank and A.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Griewank and A

Reference 18

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This paper cites Enabling user-driven Checkpointing strategies in Reverse-mode Automatic Differentiation.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Enabling user-driven Checkpointing strategies in Reverse-mode Automatic Differentiation

Reference 19

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Observation ec397770-73be-4d0c-ae57-6b15b8533c8c · outbound

This paper cites The tapenade automatic differentiation tool: Principles, model, and specification,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing The tapenade automatic differentiation tool: Principles, model, and specification,

Reference 21

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Observation aefb1365-7ce7-4939-95d5-f0580a9a845a · outbound

This paper cites Stateful dataflow multigraphs: A data-centric model for performance portability on heterogeneous architectures,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Stateful dataflow multigraphs: A data-centric model for performance portability on heterogeneous architectures,

Reference 22

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This paper cites Open neural network exchange (onnx),.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Open neural network exchange (onnx),

Reference 23

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Observation f63c47c6-e73c-4dc0-900e-6e4a661cf338 · outbound

This paper cites Npbench: A benchmarking suite for high-performance numpy,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Npbench: A benchmarking suite for high-performance numpy,

Reference 24

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This paper cites Array Programming with NumPy.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Array Programming with NumPy

Reference 25

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This paper cites A Data-Centric Optimization Framework for Machine Learning.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing A Data-Centric Optimization Framework for Machine Learning

Reference 26

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This paper cites Spivak, Calculus, 3rd ed.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Spivak, Calculus, 3rd ed

Reference 27

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DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Unresolved cited work

Reference 28

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Observation 9ebe8031-fd5e-4d7b-9e54-f11581f124f2 · outbound

This paper cites Automatic differentiation of parallel loops with formal methods,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Automatic differentiation of parallel loops with formal methods,

Reference 30

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This paper cites The complex-step derivative approximation,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing The complex-step derivative approximation,

Reference 31

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This paper cites John Wiley & Sons, Ltd, 2020, ch.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing John Wiley & Sons, Ltd, 2020, ch

Reference 32

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This paper cites The icon (icosahedral non-hydrostatic) modelling framework of dwd and mpi-m: Description of the non-hydrostatic dynamical core,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing The icon (icosahedral non-hydrostatic) modelling framework of dwd and mpi-m: Description of the non-hydrostatic dynamical core,

Reference 33

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This paper cites Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results,

Reference 34

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DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Unresolved cited work

Reference 35

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This paper cites Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/ 9781119606475.ch1.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/ 9781119606475.ch1

Reference 36

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This paper cites Dense linear algebra solvers for multicore with gpu accelerators,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Dense linear algebra solvers for multicore with gpu accelerators,

Reference 37

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This paper cites Adijac – automatic differentiation of java classfiles,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Adijac – automatic differentiation of java classfiles,

Reference 38

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This paper cites Available: https://doi.org/10.1145/2807591.2807644.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Available: https://doi.org/10.1145/2807591.2807644

Reference 39

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This paper cites Llvm compiler infrastructure,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Llvm compiler infrastructure,

Reference 40

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Observation d1472ef1-29b6-49a7-82d3-d73b022c913e · outbound

This paper cites Anatomy of high-performance matrix multiplication,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Anatomy of high-performance matrix multiplication,

Reference 41

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This paper cites Scalable automatic differen- tiation of multiple parallel paradigms through compiler augmentation,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Scalable automatic differen- tiation of multiple parallel paradigms through compiler augmentation,

Reference 42

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This paper cites Schanen, S.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Schanen, S

Reference 43

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This paper cites {TensorFlow}: a system for {Large-Scale} machine learning,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing {TensorFlow}: a system for {Large-Scale} machine learning,

Reference 44

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This paper cites Memory-Efficient Backpropagation Through Time.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Memory-Efficient Backpropagation Through Time

Reference 45

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This paper cites Reverse-mode automatic differentiation and optimization of gpu kernels via enzyme,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Reverse-mode automatic differentiation and optimization of gpu kernels via enzyme,

Reference 46

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This paper cites Training Deep Nets with Sublinear Memory Cost.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Training Deep Nets with Sublinear Memory Cost

Reference 49

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Observation 7cada5e3-1e61-438e-9d9c-912a4973aaea · outbound

This paper cites Available: http://arxiv.org/abs/1911.13214.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Available: http://arxiv.org/abs/1911.13214

Reference 54

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Observation bb4aef34-595c-4c07-b0fc-34edf1fe528d · outbound

This paper cites Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation,.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation,

Reference 57

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Observation 12c14c94-2aa5-4e7c-b3da-b2e8cc0483bb · outbound

This paper cites Julia: A Fast Dynamic Language for Technical Computing.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Julia: A Fast Dynamic Language for Technical Computing

Reference 2012

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Observation 9578e571-17d4-4fb7-9566-f379dbf0c2c2 · outbound

This paper cites Automatic differentiation in machine learning: a survey.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Automatic differentiation in machine learning: a survey

Reference 2015

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Observation e641ad89-2d96-406c-9d69-99ce535abf8c · outbound

This paper cites Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization

Reference 2019

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Observation 78aee140-aa55-43c0-865b-c7a6badfba57 · outbound

This paper cites Memory Optimization for Deep Networks.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Memory Optimization for Deep Networks

Reference 2020

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This paper cites Source-to-Source Automatic Differentiation of OpenMP Parallel Loops.

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing Source-to-Source Automatic Differentiation of OpenMP Parallel Loops

Reference 2021

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