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...
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- erasure coding 7
- coding theory 7
- distributed computing 7
- information theory 7
- matrix multiplication 5
- machine learning 2
- symbolic-numeric computing 2
- tensors 1
- random codes 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
- gradient descent 1
- Stochastic optimal control 1
- Optimization algorithms 1
- Constrained control 1
erasure coding
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...
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
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
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...
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
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
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...
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
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
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...
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
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
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...
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
symbolic-numeric computing
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
tensors
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
random codes
Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...
symbolic computing
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
commutative algebra
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
differential algebra
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
F4 Algorithm
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
Weighted Monomial Ordering
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
Parameter Identifiability
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
ODE Systems
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
Mathematical Biology
Obtaining weights for Gröbner basis computation in parameter identifiability problems
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
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...