Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
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- erasure coding 7
- coding theory 7
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- Constrained control 1
- Symbolic-numeric computing 1
- Root-finding 1
- Polynomial algorithms 1
- Computer algebra 1
- symbolic computing 1
- commutative algebra 1
- differential algebra 1
- F4 Algorithm 1
- Weighted Monomial Ordering 1
- Parameter Identifiability 1
- ODE Systems 1
- Mathematical Biology 1
- Information Theory 1
- Distributed, Parallel, and Cluster Computing (cs.DC) 1
- Discrete Mathematics 1
- Algebraic Geometry 1
- tensors 1
- random codes 1
erasure coding
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
Rook Coding for Batch Matrix Multiplication
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
Coded Matrix Chain Multiplication
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single se...
Straggler-free Coding for Concurrent Matrix Multiplications
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, ...
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are i...
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Matrix multiplication is a fundamental building block in various machine learning algorithms. When the matrix comes from a large dataset, the multiplication ...
coding theory
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
Rook Coding for Batch Matrix Multiplication
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
Coded Matrix Chain Multiplication
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single se...
Straggler-free Coding for Concurrent Matrix Multiplications
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, ...
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are i...
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Matrix multiplication is a fundamental building block in various machine learning algorithms. When the matrix comes from a large dataset, the multiplication ...
distributed computing
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
Rook Coding for Batch Matrix Multiplication
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
Coded Matrix Chain Multiplication
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single se...
Straggler-free Coding for Concurrent Matrix Multiplications
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, ...
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are i...
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Matrix multiplication is a fundamental building block in various machine learning algorithms. When the matrix comes from a large dataset, the multiplication ...
information theory
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
Rook Coding for Batch Matrix Multiplication
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
Coded Matrix Chain Multiplication
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single se...
Straggler-free Coding for Concurrent Matrix Multiplications
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, ...
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are i...
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Matrix multiplication is a fundamental building block in various machine learning algorithms. When the matrix comes from a large dataset, the multiplication ...
matrix multiplication
Rook Coding for Batch Matrix Multiplication
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
Coded Matrix Chain Multiplication
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single se...
Straggler-free Coding for Concurrent Matrix Multiplications
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, ...
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are i...
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Matrix multiplication is a fundamental building block in various machine learning algorithms. When the matrix comes from a large dataset, the multiplication ...
machine learning
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
symbolic-numeric computing
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
gradient descent
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
Stochastic optimal control
Quantile Formulation for Optimization under a Qualitative Risk Constraint
This paper focuses on the pathologies of common gradient-based algorithms for solving optimization problems under probability constraints. These problems are...
Optimization algorithms
Quantile Formulation for Optimization under a Qualitative Risk Constraint
This paper focuses on the pathologies of common gradient-based algorithms for solving optimization problems under probability constraints. These problems are...
Constrained control
Quantile Formulation for Optimization under a Qualitative Risk Constraint
This paper focuses on the pathologies of common gradient-based algorithms for solving optimization problems under probability constraints. These problems are...
Symbolic-numeric computing
Root-Squaring for Root-Finding
The root-squaring iterations of Dandelin (1826), Lobachevsky (1834), and Gräffe (1837) recursively produce the coefficients of polynomials whose zeros are th...
Root-finding
Root-Squaring for Root-Finding
The root-squaring iterations of Dandelin (1826), Lobachevsky (1834), and Gräffe (1837) recursively produce the coefficients of polynomials whose zeros are th...
Polynomial algorithms
Root-Squaring for Root-Finding
The root-squaring iterations of Dandelin (1826), Lobachevsky (1834), and Gräffe (1837) recursively produce the coefficients of polynomials whose zeros are th...
Computer algebra
Root-Squaring for Root-Finding
The root-squaring iterations of Dandelin (1826), Lobachevsky (1834), and Gräffe (1837) recursively produce the coefficients of polynomials whose zeros are th...
symbolic computing
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
commutative algebra
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
differential algebra
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
F4 Algorithm
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
Weighted Monomial Ordering
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
Parameter Identifiability
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
ODE Systems
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
Mathematical Biology
Faster Groebner bases for Lie derivatives of ODE systems via monomial orderings
Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial o...
Information Theory
Algebraic Geometric Rook Codes for Coded Distributed Computing
Tensors are a fundamental operation in distributed and are commonly distributed into multiple parallel tasks for large datasets. Stragglers and other failure...
Distributed, Parallel, and Cluster Computing (cs.DC)
Algebraic Geometric Rook Codes for Coded Distributed Computing
Tensors are a fundamental operation in distributed and are commonly distributed into multiple parallel tasks for large datasets. Stragglers and other failure...
Discrete Mathematics
Algebraic Geometric Rook Codes for Coded Distributed Computing
Tensors are a fundamental operation in distributed and are commonly distributed into multiple parallel tasks for large datasets. Stragglers and other failure...
Algebraic Geometry
Algebraic Geometric Rook Codes for Coded Distributed Computing
Tensors are a fundamental operation in distributed and are commonly distributed into multiple parallel tasks for large datasets. Stragglers and other failure...
tensors
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...
random codes
Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
We extend coded distributed computing over finite fields to allow the number of workers to be larger than the field size. We give codes that work for fully g...