Add to ORKGWe provide an efficient implementation of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware
Significant success has been reported recently ucsing deep neural networks for classification. Such ...
This paper presents some simple techniques to improve the backpropagation algorithm. Since learning ...
One of the major issues in using artificial neural networks is reducing the training and the testing...
Recently, sparse training methods have started to be established as a de facto approach for training...
In studies of neural networks, the Multilavered Feedforward Network is the most widely used network ...
Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is ...
Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-based methods, such as...
Abstract — In this paper, we present an efficient technique for mapping a backpropagation (BP) learn...
The growing energy and performance costs of deep learning have driven the community to reduce the si...
this report also have been published on ESANN '93 [Schiffmann et al., 1993]. The dataset used i...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...
Artificial Neural Networks (ANNs) have emerged as hot topics in the research community. Despite the ...
The over-parameterization of neural networks and the local optimality of backpropagation algorithm h...
Motivated by the goal of enabling energy-efficient and/or lower-cost hardware implementations of dee...
Existing approaches that partition a convolutional neural network (CNN) onto multiple accelerators a...
Significant success has been reported recently ucsing deep neural networks for classification. Such ...
This paper presents some simple techniques to improve the backpropagation algorithm. Since learning ...
One of the major issues in using artificial neural networks is reducing the training and the testing...
Recently, sparse training methods have started to be established as a de facto approach for training...
In studies of neural networks, the Multilavered Feedforward Network is the most widely used network ...
Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is ...
Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-based methods, such as...
Abstract — In this paper, we present an efficient technique for mapping a backpropagation (BP) learn...
The growing energy and performance costs of deep learning have driven the community to reduce the si...
this report also have been published on ESANN '93 [Schiffmann et al., 1993]. The dataset used i...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...
Artificial Neural Networks (ANNs) have emerged as hot topics in the research community. Despite the ...
The over-parameterization of neural networks and the local optimality of backpropagation algorithm h...
Motivated by the goal of enabling energy-efficient and/or lower-cost hardware implementations of dee...
Existing approaches that partition a convolutional neural network (CNN) onto multiple accelerators a...
Significant success has been reported recently ucsing deep neural networks for classification. Such ...
This paper presents some simple techniques to improve the backpropagation algorithm. Since learning ...
One of the major issues in using artificial neural networks is reducing the training and the testing...