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228
Representation learning: A review and new perspectives.
 of IEEE Conf. Comp. Vision Pattern Recog. (CVPR),
, 2005
"... AbstractThe success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can b ..."
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Cited by 173 (4 self)
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AbstractThe success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representationlearning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
Multimodal learning with deep boltzmann machines
 In NIPS’2012
, 2012
"... Data often consists of multiple diverse modalities. For example, images are tagged with textual information and videos are accompanied by audio. Each modality is characterized by having distinct statistical properties. We propose a Deep Boltzmann Machine for learning a generative model of such mult ..."
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Cited by 77 (2 self)
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Data often consists of multiple diverse modalities. For example, images are tagged with textual information and videos are accompanied by audio. Each modality is characterized by having distinct statistical properties. We propose a Deep Boltzmann Machine for learning a generative model of such multimodal data. We show that the model can be used to create fused representations by combining features across modalities. These learned representations are useful for classification and information retrieval. By sampling from the conditional distributions over each data modality, it is possible to create these representations even when some data modalities are missing. We conduct experiments on bimodal imagetext and audiovideo data. The fused representation achieves good classification results on the MIRFlickr data set matching or outperforming other deep models as well as SVM based models that use Multiple Kernel Learning. We further demonstrate that this multimodal model helps classification and retrieval even when only unimodal data is available at test time.
SumProduct Networks: A New Deep Architecture
"... The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are general conditions under which the partition function is tractable? The answer leads to a new kind of deep architecture, which we call sumproduct networks ..."
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Cited by 73 (10 self)
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The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are general conditions under which the partition function is tractable? The answer leads to a new kind of deep architecture, which we call sumproduct networks (SPNs). SPNs are directed acyclic graphs with variables as leaves, sums and products as internal nodes, and weighted edges. We show that if an SPN is complete and consistent it represents the partition function and all marginals of some graphical model, and give semantics to its nodes. Essentially all tractable graphical models can be cast as SPNs, but SPNs are also strictly more general. We then propose learning algorithms for SPNs, based on backpropagation and EM. Experiments show that inference and learning with SPNs can be both faster and more accurate than with standard deep networks. For example, SPNs perform image completion better than stateoftheart deep networks for this task. SPNs also have intriguing potential connections to the architecture of the cortex. 1
Maxout networks
 In ICML
, 2013
"... We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to bot ..."
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Cited by 68 (17 self)
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We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout’s fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR10, CIFAR100, and SVHN.
Learning multiple layers of representations
 Trends in Cognitive Sciences 11:428–434
, 2007
"... To achieve its ’ impressive performance at tasks such as speech or object recognition, the brain extracts multiple levels of representation from the sensory input. Backpropagation was the first computationally efficient model of how neural networks could learn multiple layers of representation, but ..."
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Cited by 55 (3 self)
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To achieve its ’ impressive performance at tasks such as speech or object recognition, the brain extracts multiple levels of representation from the sensory input. Backpropagation was the first computationally efficient model of how neural networks could learn multiple layers of representation, but it required labeled training data and it did not work well in deep networks. The limitations of backpropagation learning can now be overcome by using multilayer neural networks that contain topdown connections and training them to generate sensory data rather than to classify it. Learning multilayer generative models appears to be difficult, but a recent discovery makes it easy to learn nonlinear, distributed representations one layer at a time. The multiple layers of representation learned in this way can subsequently be finetuned to produce generative or discriminative models that work much better than previous approaches. Learning feature detectors
Unsupervised Feature Learning for RGBD Based Object Recognition
 In International Symposium on Experimental Robotics (ISER
, 2012
"... Abstract Recently introduced RGBD cameras are capable of providing high quality synchronized videos of both color and depth. With its advanced sensing capabilities, this technology represents an opportunity to dramatically increase the capabilities of object recognition. It also raises the problem ..."
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Cited by 51 (6 self)
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Abstract Recently introduced RGBD cameras are capable of providing high quality synchronized videos of both color and depth. With its advanced sensing capabilities, this technology represents an opportunity to dramatically increase the capabilities of object recognition. It also raises the problem of developing expressive features for the color and depth channels of these sensors. In this paper we introduce hierarchical matching pursuit (HMP) for RGBD data. HMP uses sparse coding to learn hierarchical feature representations from raw RGBD data in an unsupervised way. Extensive experiments on various datasets indicate that the features learned with our approach enable superior object recognition results using linear support vector machines. 1
Dropout: A simple way to prevent neural networks from overfitting
 Journal of Machine Learning Research
, 1929
"... 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 net ..."
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Cited by 49 (3 self)
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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 coadapting 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 stateoftheart results on many benchmark data sets.
The Neural Autoregressive Distribution Estimator
 In AISTATS’2011
, 2011
"... We describe a new approach for modeling the distribution of highdimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable dist ..."
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Cited by 44 (6 self)
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We describe a new approach for modeling the distribution of highdimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable distribution estimator, since evaluating the probability it assigns to some given observation requires the computation of the socalled partition function, which itself is intractable for RBMs of even moderate size. Our model circumvents this difficulty by decomposing the joint distribution of observations into tractable conditional distributions and modeling each conditional using a nonlinear function similar to a conditional of an RBM. Our model can also be interpreted as an autoencoder wired such that its output can be used to assign valid probabilities to observations. We show that this new model outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM. 1
Learning in markov random fields using tempered transitions
 In Advances in Neural Information Processing Systems
"... Markov random fields (MRF’s), or undirected graphical models, provide a powerful framework for modeling complex dependencies among random variables. Maximum likelihood learning in MRF’s is hard due to the presence of the global normalizing constant. In this paper we consider a class of stochastic ap ..."
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Cited by 36 (2 self)
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Markov random fields (MRF’s), or undirected graphical models, provide a powerful framework for modeling complex dependencies among random variables. Maximum likelihood learning in MRF’s is hard due to the presence of the global normalizing constant. In this paper we consider a class of stochastic approximation algorithms of the RobbinsMonro type that use Markov chain Monte Carlo to do approximate maximum likelihood learning. We show that using MCMC operators based on tempered transitions enables the stochastic approximation algorithm to better explore highly multimodal distributions, which considerably improves parameter estimates in large, denselyconnected MRF’s. Our results on MNIST and NORB datasets demonstrate that we can successfully learn good generative models of highdimensional, richly structured data that perform well on digit and object recognition tasks. 1