We revisit the Frank-Wolfe algorithm for constrained convex optimization and show that it can be implemented as a simple recurrent neural network with softmin activation functions. As an example for a practical application of this result, we discuss how to train such a network to act as an associative memory
The Frank-Wolfe (FW) method, which implements efficient linear oracles that minimize linear approxim...
Convexity has recently received a lot of attention in the machine learning community, and the lack o...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
The move from hand-designed to learned optimizers in machine learning has been quite successful for ...
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dim...
AbstractMany optimization procedures presume the availability of an initial approximation in the nei...
AbstractThis paper presents a neural network approach for solving convex programming problems with e...
We show how to implement a simple procedure for support vector machine training as a recurrent neura...
Artificial Neural Networks are a supervised machine learning technique with a number of drawbacks. T...
Constrained optimization problems arise widely in scientific research and engineering applications. ...
We investigate the qualitative properties of a recurrent neural network (RNN) for minimizing a nonli...
The recurrent neural network approach to combinatorial optimization has during the last decade evolv...
Aiming at convex optimization under structural constraints, this work introduces and analyzes a vari...
This paper is concerned with neural networks which have the ability to solve linear and nonlinear co...
Neural networks consist of highly interconnected and parallel nonlinear processing elements that are...
The Frank-Wolfe (FW) method, which implements efficient linear oracles that minimize linear approxim...
Convexity has recently received a lot of attention in the machine learning community, and the lack o...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
The move from hand-designed to learned optimizers in machine learning has been quite successful for ...
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dim...
AbstractMany optimization procedures presume the availability of an initial approximation in the nei...
AbstractThis paper presents a neural network approach for solving convex programming problems with e...
We show how to implement a simple procedure for support vector machine training as a recurrent neura...
Artificial Neural Networks are a supervised machine learning technique with a number of drawbacks. T...
Constrained optimization problems arise widely in scientific research and engineering applications. ...
We investigate the qualitative properties of a recurrent neural network (RNN) for minimizing a nonli...
The recurrent neural network approach to combinatorial optimization has during the last decade evolv...
Aiming at convex optimization under structural constraints, this work introduces and analyzes a vari...
This paper is concerned with neural networks which have the ability to solve linear and nonlinear co...
Neural networks consist of highly interconnected and parallel nonlinear processing elements that are...
The Frank-Wolfe (FW) method, which implements efficient linear oracles that minimize linear approxim...
Convexity has recently received a lot of attention in the machine learning community, and the lack o...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
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