Add to ORKGCurrent training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the optimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis...
Deep neural networks have achieved significant success in a number of challenging engineering proble...
Neural networks, as part of deep learning, have become extremely pop- ular due to their ability to e...
Developments in deep learning with ANNs (Artificial Neural Networks) are paving the way for revoluti...
Current training methods for deep neural networks boil down to very high dimensional and non-convex ...
In the recent decade, deep neural networks have solved ever more complex tasks across many fronts in...
In the past decade, neural networks have demonstrated impressive performance in supervised learning....
This thesis characterizes the training process of deep neural networks. We are driven by two apparen...
In this dissertation, we are concerned with the advancement of optimization algorithms for training ...
The Multi-Layer Perceptron (MLP) is one of the most widely applied and researched Artificial Neural ...
Despite the fact that the loss functions of deep neural networks are highly non-convex,gradient-base...
Recent work has established clear links between the generalization performance of trained neural net...
Neural networks are an important class of highly flexible and powerful models inspired by the struct...
Training deep neural networks is inherently subject to the predefined and fixed loss functions durin...
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dim...
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing i...
Deep neural networks have achieved significant success in a number of challenging engineering proble...
Neural networks, as part of deep learning, have become extremely pop- ular due to their ability to e...
Developments in deep learning with ANNs (Artificial Neural Networks) are paving the way for revoluti...
Current training methods for deep neural networks boil down to very high dimensional and non-convex ...
In the recent decade, deep neural networks have solved ever more complex tasks across many fronts in...
In the past decade, neural networks have demonstrated impressive performance in supervised learning....
This thesis characterizes the training process of deep neural networks. We are driven by two apparen...
In this dissertation, we are concerned with the advancement of optimization algorithms for training ...
The Multi-Layer Perceptron (MLP) is one of the most widely applied and researched Artificial Neural ...
Despite the fact that the loss functions of deep neural networks are highly non-convex,gradient-base...
Recent work has established clear links between the generalization performance of trained neural net...
Neural networks are an important class of highly flexible and powerful models inspired by the struct...
Training deep neural networks is inherently subject to the predefined and fixed loss functions durin...
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dim...
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing i...
Deep neural networks have achieved significant success in a number of challenging engineering proble...
Neural networks, as part of deep learning, have become extremely pop- ular due to their ability to e...
Developments in deep learning with ANNs (Artificial Neural Networks) are paving the way for revoluti...