Representation Learning: A Review and New Perspectives

The 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 representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, 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

Efficient Learning for Undirected Topic Models

Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient. This paper provides a novel estimator to speed up the learning based on Noise Contrastive Estimate, extended for documents of variant lengths and weighted inputs. Experiments on two benchmarks show that the new estimator achieves great learning efficiency and high accuracy on document retrieval and classification.Comment: Accepted by ACL-IJCNLP 2015 short paper. 6 page

Practical recommendations for gradient-based training of deep architectures

Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyper-parameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures

Advances in scaling deep learning algorithms

Les algorithmes d'apprentissage profond forment un nouvel ensemble de méthodes puissantes pour l'apprentissage automatique. L'idée est de combiner des couches de facteurs latents en hierarchies. Cela requiert souvent un coût computationel plus elevé et augmente aussi le nombre de paramètres du modèle. Ainsi, l'utilisation de ces méthodes sur des problèmes à plus grande échelle demande de réduire leur coût et aussi d'améliorer leur régularisation et leur optimization. Cette thèse adresse cette question sur ces trois perspectives. Nous étudions tout d'abord le problème de réduire le coût de certains algorithmes profonds. Nous proposons deux méthodes pour entrainer des machines de Boltzmann restreintes et des auto-encodeurs débruitants sur des distributions sparses à haute dimension. Ceci est important pour l'application de ces algorithmes pour le traitement de langues naturelles. Ces deux méthodes (Dauphin et al., 2011; Dauphin and Bengio, 2013) utilisent l'échantillonage par importance pour échantilloner l'objectif de ces modèles. Nous observons que cela réduit significativement le temps d'entrainement. L'accéleration atteint 2 ordres de magnitude sur plusieurs bancs d'essai. Deuxièmement, nous introduisont un puissant régularisateur pour les méthodes profondes. Les résultats expérimentaux démontrent qu'un bon régularisateur est crucial pour obtenir de bonnes performances avec des gros réseaux (Hinton et al., 2012). Dans Rifai et al. (2011), nous proposons un nouveau régularisateur qui combine l'apprentissage non-supervisé et la propagation de tangente (Simard et al., 1992). Cette méthode exploite des principes géometriques et permit au moment de la publication d'atteindre des résultats à l'état de l'art. Finalement, nous considérons le problème d'optimiser des surfaces non-convexes à haute dimensionalité comme celle des réseaux de neurones. Tradionellement, l'abondance de minimum locaux était considéré comme la principale difficulté dans ces problèmes. Dans Dauphin et al. (2014a) nous argumentons à partir de résultats en statistique physique, de la théorie des matrices aléatoires, de la théorie des réseaux de neurones et à partir de résultats expérimentaux qu'une difficulté plus profonde provient de la prolifération de points-selle. Dans ce papier nous proposons aussi une nouvelle méthode pour l'optimisation non-convexe.Deep learning algorithms are a new set of powerful methods for machine learning. The general idea is to combine layers of latent factors into hierarchies. This usually leads to a higher computational cost and having more parameters to tune. Thus scaling to larger problems will require not only reducing their computational cost but also improving regularization and optimization. This thesis investigates scaling from these three perspectives. We first study the problem of reducing the computational cost of some deep learning algorithms. We propose methods to scale restricted Boltzmann machines (RBM) and denoising auto-encoders (DAE) to very high-dimensional sparse distributions. This is important for applications of deep learning to natural language processing. Both methods (Dauphin et al., 2011; Dauphin and Bengio, 2013) rely on importance sampling to subsample the learning objective of these models. We show that this greatly reduces the training time, leading to 2 orders of magnitude speed ups on several benchmark datasets without losses in the quality of the model. Second, we introduce a powerful regularization method for deep neural nets. Experiments have shown that proper regularization is in many cases crucial to obtaining good performance out of larger networks (Hinton et al., 2012). In Rifai et al. (2011), we propose a new regularizer that combines unsupervised learning and tangent propagation (Simard et al., 1992). The method exploits several geometrical insights and was able at the time of publication to reach state-of-the-art results on competitive benchmarks. Finally, we consider the problem of optimizing over high-dimensional non-convex loss surfaces like those found in deep neural nets. Traditionally, the main difficulty in these problems is considered to be the abundance of local minima. In Dauphin et al. (2014a) we argue, based on results from statistical physics, random matrix theory, neural network theory, and empirical evidence, that the vast majority of critical points are saddle points, not local minima. We also propose a new optimization method for non-convex optimization

Deep Learning of Representations: Looking Forward

Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these challenges

Multimodal learning from visual and remotely sensed data

Autonomous vehicles are often deployed to perform exploration and monitoring missions in unseen environments. In such applications, there is often a compromise between the information richness and the acquisition cost of different sensor modalities. Visual data is usually very information-rich, but requires in-situ acquisition with the robot. In contrast, remotely sensed data has a larger range and footprint, and may be available prior to a mission. In order to effectively and efficiently explore and monitor the environment, it is critical to make use of all of the sensory information available to the robot. One important application is the use of an Autonomous Underwater Vehicle (AUV) to survey the ocean floor. AUVs can take high resolution in-situ photographs of the sea floor, which can be used to classify different regions into various habitat classes that summarise the observed physical and biological properties. This is known as benthic habitat mapping. However, since AUVs can only image a tiny fraction of the ocean floor, habitat mapping is usually performed with remotely sensed bathymetry (ocean depth) data, obtained from shipborne multibeam sonar. With the recent surge in unsupervised feature learning and deep learning techniques, a number of previous techniques have investigated the concept of multimodal learning: capturing the relationship between different sensor modalities in order to perform classification and other inference tasks. This thesis proposes related techniques for visual and remotely sensed data, applied to the task of autonomous exploration and monitoring with an AUV. Doing so enables more accurate classification of the benthic environment, and also assists autonomous survey planning. The first contribution of this thesis is to apply unsupervised feature learning techniques to marine data. The proposed techniques are used to extract features from image and bathymetric data separately, and the performance is compared to that with more traditionally used features for each sensor modality. The second contribution is the development of a multimodal learning architecture that captures the relationship between the two modalities. The model is robust to missing modalities, which means it can extract better features for large-scale benthic habitat mapping, where only bathymetry is available. The model is used to perform classification with various combinations of modalities, demonstrating that multimodal learning provides a large performance improvement over the baseline case. The third contribution is an extension of the standard learning architecture using a gated feature learning model, which enables the model to better capture the ‘one-to-many’ relationship between visual and bathymetric data. This opens up further inference capabilities, with the ability to predict visual features from bathymetric data, which allows image-based queries. Such queries are useful for AUV survey planning, especially when supervised labels are unavailable. The final contribution is the novel derivation of a number of information-theoretic measures to aid survey planning. The proposed measures predict the utility of unobserved areas, in terms of the amount of expected additional visual information. As such, they are able to produce utility maps over a large region that can be used by the AUV to determine the most informative locations from a set of candidate missions. The models proposed in this thesis are validated through extensive experiments on real marine data. Furthermore, the introduced techniques have applications in various other areas within robotics. As such, this thesis concludes with a discussion on the broader implications of these contributions, and the future research directions that arise as a result of this work

Improving Deep Representation Learning with Complex and Multimodal Data.

Representation learning has emerged as a way to learn meaningful representation from data and made a breakthrough in many applications including visual object recognition, speech recognition, and text understanding. However, learning representation from complex high-dimensional sensory data is challenging since there exist many irrelevant factors of variation (e.g., data transformation, random noise). On the other hand, to build an end-to-end prediction system for structured output variables, one needs to incorporate probabilistic inference to properly model a mapping from single input to possible configurations of output variables. This thesis addresses limitations of current representation learning in two parts. The first part discusses efficient learning algorithms of invariant representation based on restricted Boltzmann machines (RBMs). Pointing out the difficulty of learning, we develop an efficient initialization method for sparse and convolutional RBMs. On top of that, we develop variants of RBM that learn representations invariant to data transformations such as translation, rotation, or scale variation by pooling the filter responses of input data after a transformation, or to irrelevant patterns such as random or structured noise, by jointly performing feature selection and feature learning. We demonstrate improved performance on visual object recognition and weakly supervised foreground object segmentation. The second part discusses conditional graphical models and learning frameworks for structured output variables using deep generative models as prior. For example, we combine the best properties of the CRF and the RBM to enforce both local and global (e.g., object shape) consistencies for visual object segmentation. Furthermore, we develop a deep conditional generative model of structured output variables, which is an end-to-end system trainable by backpropagation. We demonstrate the importance of global prior and probabilistic inference for visual object segmentation. Second, we develop a novel multimodal learning framework by casting the problem into structured output representation learning problems, where the output is one data modality to be predicted from the other modalities, and vice versa. We explain as to how our method could be more effective than maximum likelihood learning and demonstrate the state-of-the-art performance on visual-text and visual-only recognition tasks.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113549/1/kihyuks_1.pd