Results 1  10
of
969
Greedy layerwise training of deep networks
, 2006
"... Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multilayer neural networks have many levels of nonlinearities allow ..."
Abstract

Cited by 394 (48 self)
 Add to MetaCart
(Show Context)
Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multilayer neural networks have many levels of nonlinearities allowing them to compactly represent highly nonlinear and highlyvarying functions. However, until recently it was not clear how to train such deep networks, since gradientbased optimization starting from random initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy layerwise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success and extend it to cases where the inputs are continuous or where the structure of the input distribution is not revealing enough about the variable to be predicted in a supervised task. Our experiments also conrm the hypothesis that the greedy layerwise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are highlevel abstractions of the input, bringing better generalization.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
 IN ICML’09
, 2009
"... ..."
Deep Neural Networks for Acoustic Modeling in Speech Recognition
"... Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative ..."
Abstract

Cited by 272 (47 self)
 Add to MetaCart
(Show Context)
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feedforward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks with many hidden layers, that are trained using new methods have been shown to outperform Gaussian mixture models on a variety of speech recognition benchmarks, sometimes by a large margin. This paper provides an overview of this progress and represents the shared views of four research groups who have had recent successes in using deep neural networks for acoustic modeling in speech recognition. I.
ContextDependent Pretrained Deep Neural Networks for Large Vocabulary Speech Recognition
 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING
, 2012
"... We propose a novel contextdependent (CD) model for large vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pretrained deep neural network hidden Markov model (DNNHMM) hybrid architecture that trains the DNN to pr ..."
Abstract

Cited by 254 (50 self)
 Add to MetaCart
We propose a novel contextdependent (CD) model for large vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pretrained deep neural network hidden Markov model (DNNHMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep belief network pretraining algorithm is a robust and often helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. We illustrate the key components of our model, describe the procedure for applying CDDNNHMMs to LVSR, and analyze the effects of various modeling choices on performance. Experiments on a challenging business search dataset demonstrate that CDDNNHMMs can significantly outperform the conventional contextdependent Gaussian mixture model (GMM)HMMs, with an absolute sentence accuracy improvement of 5.8 % and 9.2 % (or relative error reduction of 16.0 % and 23.2%) over the CDGMMHMMs trained using the minimum phone error rate (MPE) and maximum likelihood (ML) criteria, respectively.
Extracting and Composing Robust Features with Denoising Autoencoders
, 2008
"... Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a repre ..."
Abstract

Cited by 251 (32 self)
 Add to MetaCart
(Show Context)
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite.
Natural language processing (almost) from scratch
, 2011
"... We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including partofspeech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid taskspecific eng ..."
Abstract

Cited by 248 (18 self)
 Add to MetaCart
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including partofspeech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid taskspecific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting manmade input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.
Boltzmann machines
, 2007
"... A Boltzmann Machine is a network of symmetrically connected, neuronlike units that make stochastic decisions about whether to be on or off. Boltzmann machines have a simple learning algorithm that allows them to discover interesting features in datasets composed of binary vectors. The learning algor ..."
Abstract

Cited by 228 (21 self)
 Add to MetaCart
A Boltzmann Machine is a network of symmetrically connected, neuronlike units that make stochastic decisions about whether to be on or off. Boltzmann machines have a simple learning algorithm that allows them to discover interesting features in datasets composed of binary vectors. The learning algorithm is very slow in networks with many layers of feature detectors, but it can be made much faster by learning one layer of feature detectors at a time. Boltzmann machines are used to solve two quite different computational problems. For a search problem, the weights on the connections are fixed and are used to represent the cost function of an optimization problem. The stochastic dynamics of a Boltzmann machine then allow it to sample binary state vectors that represent good solutions to the optimization problem. For a learning problem, the Boltzmann machine is shown a set of binary data vectors and it must find weights on the connections so that the data vectors are good solutions to the optimization problem defined by those weights. To solve a learning problem, Boltzmann machines make many small updates to their weights, and each update requires them to solve many different search problems. The stochastic dynamics of a Boltzmann machine When unit i is given the opportunity to update its binary state, it first computes its total input, zi, which is the sum of its own bias, bi, and the weights on connections coming from other active units: zi = bi + �
An Analysis of SingleLayer Networks in Unsupervised Feature Learning
"... A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning algorithms and deep models. In this paper, however, we show that several ..."
Abstract

Cited by 223 (19 self)
 Add to MetaCart
(Show Context)
A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning algorithms and deep models. In this paper, however, we show that several very simple factors, such as the number of hidden nodes in the model, may be as important to achieving high performance as the choice of learning algorithm or the depth of the model. Specifically, we will apply several offtheshelf feature learning algorithms (sparse autoencoders, sparse RBMs and Kmeans clustering, Gaussian mixtures) to NORB and CIFAR datasets using only singlelayer networks. We then present a detailed analysis of the effect of changes in the model setup: the receptive field size, number of hidden nodes (features), the stepsize (“stride”) between extracted features, and the effect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are as critical to achieving high performance as the choice of algorithm itself—so critical, in fact, that when these parameters are pushed to their limits, we are able to achieve stateoftheart performance on both CIFAR and NORB using only a single layer of features. More surprisingly, our best performance is based on Kmeans clustering, which is extremely fast, has no hyperparameters to tune beyond the model structure itself, and is very easy implement. Despite the simplicity of our system, we achieve performance beyond all previously published results on the CIFAR10 and NORB datasets (79.6 % and 97.0 % accuracy respectively). 1
Restricted Boltzmann machines for collaborative filtering
 In Machine Learning, Proceedings of the Twentyfourth International Conference (ICML 2004). ACM
, 2007
"... Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of twolayer undirected graphical models, called Restricted Boltzmann Machines (RBM’s), can be used to model tabular data, such as user’s ratings of movies. We present eff ..."
Abstract

Cited by 220 (12 self)
 Add to MetaCart
(Show Context)
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of twolayer undirected graphical models, called Restricted Boltzmann Machines (RBM’s), can be used to model tabular data, such as user’s ratings of movies. We present efficient learning and inference procedures for this class of models and demonstrate that RBM’s can be successfully applied to the Netflix data set, containing over 100 million user/movie ratings. We also show that RBM’s slightly outperform carefullytuned SVD models. When the predictions of multiple RBM models and multiple SVD models are linearly combined, we achieve an error rate that is well over 6 % better than the score of Netflix’s own system. 1.
Efficient learning of sparse representations with an energybased model
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (NIPS 2006
, 2006
"... We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying nonlinearity that turns a code vector into a quasibinary sparse code vector. Given an input, the optimal code minimizes the distance b ..."
Abstract

Cited by 219 (15 self)
 Add to MetaCart
(Show Context)
We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying nonlinearity that turns a code vector into a quasibinary sparse code vector. Given an input, the optimal code minimizes the distance between the output of the decoder and the input patch while being as similar as possible to the encoder output. Learning proceeds in a twophase EMlike fashion: (1) compute the minimumenergy code vector, (2) adjust the parameters of the encoder and decoder so as to decrease the energy. The model produces “stroke detectors ” when trained on handwritten numerals, and Gaborlike filters when trained on natural image patches. Inference and learning are very fast, requiring no preprocessing, and no expensive sampling. Using the proposed unsupervised method to initialize the first layer of a convolutional network, we achieved an error rate slightly lower than the best reported result on the MNIST dataset. Finally, an extension of the method is described to learn topographical filter maps. 1