Open AccessJournal Article
Dropout: a simple way to prevent neural networks from overfitting
TLDR
It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.Abstract:
Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.read more
Citations
More filters
Journal ArticleDOI
Audio-driven facial animation by joint end-to-end learning of pose and emotion
TL;DR: This work presents a machine learning technique for driving 3D facial animation by audio input in real time and with low latency, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone.
Posted Content
Bayesian Compression for Deep Learning
TL;DR: This work argues that the most principled and effective way to attack the problem of compression and computational efficiency in deep learning is by adopting a Bayesian point of view, where through sparsity inducing priors the authors prune large parts of the network.
Posted Content
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
Nils Reimers,Iryna Gurevych +1 more
TL;DR: In this article, the authors show that reporting a single performance score is insufficient to compare non-deterministic approaches and propose to compare score distributions based on multiple executions instead of publishing and reporting single performance scores.
Journal ArticleDOI
A fast and robust convolutional neural network-based defect detection model in product quality control
TL;DR: The elaborately designed deep convolutional neural networks proposed by this paper can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise.
Posted Content
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
TL;DR: RS-CNN as mentioned in this paper extends regular grid CNN to irregular configuration for point cloud analysis, where the convolutional weight for local point set is forced to learn a highlevel relation expression from predefined geometric priors, between a sampled point from this point set and the others.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Dissertation
Learning Multiple Layers of Features from Tiny Images
TL;DR: In this paper, the authors describe how to train a multi-layer generative model of natural images, using a dataset of millions of tiny colour images, described in the next section.