Publication date
May 2023
DOIAbstract
Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.If you use this software, please cite this software using the metadata from "preferred-citation"
Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.If you use this software, please cite this software using the metadata from "preferred-citation"
Automated methods for computing derivatives of cost functions are essential to many modern applicati...
In this paper, we explain what are tensors and how tensors can help in computing
Mathematical operators whose transformation rules constitute the building blocks of a multi-linear a...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Tensors.jl is a Julia package that provides efficient computations with symmetric and non-symmetric ...
Tensors are higher-dimensional analogs of matrices, and represent a key data abstraction for many ap...
Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in...
Automated methods for computing derivatives of cost functions are essential to many modern applicati...
In this paper, we explain what are tensors and how tensors can help in computing
Mathematical operators whose transformation rules constitute the building blocks of a multi-linear a...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Efficient computations with symmetric and non-symmetric tensors with support for automatic different...
Tensors.jl is a Julia package that provides efficient computations with symmetric and non-symmetric ...
Tensors are higher-dimensional analogs of matrices, and represent a key data abstraction for many ap...
Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in...
Automated methods for computing derivatives of cost functions are essential to many modern applicati...
In this paper, we explain what are tensors and how tensors can help in computing
Mathematical operators whose transformation rules constitute the building blocks of a multi-linear a...
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