%0 Journal Article %1 ludwig2016learning %A Ludwig, Oswaldo %D 2016 %E ArXiv, %J ArXiv %K 2017 arxiv deep-learning eigen %N arXiv:1604.06985 %T Deep Learning with Eigenvalue Decay Regularizer %U http://arxiv.org/abs/1604.06985 %V ArXiv eprint %X This paper extends our previous work on regularization of neural networks using Eigenvalue Decay by employing a soft approximation of the dominant eigenvalue in order to enable the calculation of its derivatives in relation to the synaptic weights, and therefore the application of back-propagation, which is a primary demand for deep learning. Moreover, we extend our previous theoretical analysis to deep neural networks and multiclass classification problems. Our method is implemented as an additional regularizer in Keras, a modular neural networks library written in Python, and evaluated in the benchmark data sets Reuters Newswire Topics Classification, IMDB database for binary sentiment classification and MNIST database of handwritten digits.

@article{ludwig2016learning, abstract = {This paper extends our previous work on regularization of neural networks using Eigenvalue Decay by employing a soft approximation of the dominant eigenvalue in order to enable the calculation of its derivatives in relation to the synaptic weights, and therefore the application of back-propagation, which is a primary demand for deep learning. Moreover, we extend our previous theoretical analysis to deep neural networks and multiclass classification problems. Our method is implemented as an additional regularizer in Keras, a modular neural networks library written in Python, and evaluated in the benchmark data sets Reuters Newswire Topics Classification, IMDB database for binary sentiment classification and MNIST database of handwritten digits.}, added-at = {2017-11-29T16:02:53.000+0100}, author = {Ludwig, Oswaldo}, biburl = {https://www.bibsonomy.org/bibtex/24e00e4da0f9bd5f00322bf9f22806143/achakraborty}, description = {Deep Learning with Eigenvalue Decay Regularizer}, editor = {ArXiv}, interhash = {543ecc71f34b36959640fbade71fb5f3}, intrahash = {4e00e4da0f9bd5f00322bf9f22806143}, journal = {ArXiv}, keywords = {2017 arxiv deep-learning eigen}, month = apr, note = {cite arxiv:1604.06985}, number = {arXiv:1604.06985}, timestamp = {2017-11-29T17:53:50.000+0100}, title = {Deep Learning with Eigenvalue Decay Regularizer}, url = {http://arxiv.org/abs/1604.06985}, volume = {ArXiv eprint}, year = 2016 }