Add to ORKGIn the interest of reproducible research, this is exactly the version of the code used to generate the figures in the paper "Does a sparse ReLU network training problem always admit an optimum?" by the same authors, available at https://inria.hal.science/hal-04108849 with its detailed bibliographical notice
The leaky ReLU network with a group sparse regularization term has been widely used in the recent ye...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
International audienceIn theory, the choice of ReLU(0) in [0, 1] for a neural network has a negligib...
International audienceGiven a training set, a loss function, and a neural network architecture, it i...
Code to reproduce experiments in "Spurious Valleys, NP-hardness, and Tractability of Sparse Matrix F...
In the interest of reproducible research, this is exactly the version of the code used to generate t...
Code base for paper "Reinforcement learning prioritizes general applicability in reaction optimizati...
The success of deep learning has shown impressive empirical breakthroughs, but many theoretical ques...
In the interest of reproducible research, this is exactly the version of the code used to generate t...
. In this paper we study how global optimization methods (like genetic algorithms) can be used to tr...
At present, the most efficient machine learning techniques is deep learning, with neurons using Rect...
We consider the algorithmic problem of finding the optimal weights and biases for a two-layer fully ...
We initiate a formal study of reproducibility in optimization. We define a quantitative measure of r...
We study optimization problems where the objective function is modeled through feedforward neural ne...
The leaky ReLU network with a group sparse regularization term has been widely used in the recent ye...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
International audienceIn theory, the choice of ReLU(0) in [0, 1] for a neural network has a negligib...
International audienceGiven a training set, a loss function, and a neural network architecture, it i...
Code to reproduce experiments in "Spurious Valleys, NP-hardness, and Tractability of Sparse Matrix F...
In the interest of reproducible research, this is exactly the version of the code used to generate t...
Code base for paper "Reinforcement learning prioritizes general applicability in reaction optimizati...
The success of deep learning has shown impressive empirical breakthroughs, but many theoretical ques...
In the interest of reproducible research, this is exactly the version of the code used to generate t...
. In this paper we study how global optimization methods (like genetic algorithms) can be used to tr...
At present, the most efficient machine learning techniques is deep learning, with neurons using Rect...
We consider the algorithmic problem of finding the optimal weights and biases for a two-layer fully ...
We initiate a formal study of reproducibility in optimization. We define a quantitative measure of r...
We study optimization problems where the objective function is modeled through feedforward neural ne...
The leaky ReLU network with a group sparse regularization term has been widely used in the recent ye...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
International audienceIn theory, the choice of ReLU(0) in [0, 1] for a neural network has a negligib...