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

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Human sensing indoors in RF utilising unlabeled sensor streams

Indoor human sensing in radio frequencies is crucial for non-invasive, privacy-preserving digital healthcare, and machine learning is the backbone of such systems. Changes in the environment affect negatively the quality of learned mappings, which necessitates a semi-supervised approach that makes use of the unlabeled data stream to allow the learner to refine their hypothesis with time.We first explore the ambulation classification problem with frequency modulated continuous wave (FMCW) radar, replacing manual feature engineering by inductive bias in architectural choices of the neural network. We demonstrate that key ambulations: walk, bend, sit to stand and stand to sit can be distinguished with high accuracy. We then apply variational autoencoders to explore unsupervised localisation in synthetic grayscale images, finding that the goal is achievable with the choice of encoder that encodes temporal structure.Next, we evaluate temporal contrastive learning as the method of using unlabeled sensor streams in fingerprinting localisation, finding that it is a reliable method of defining a notion of pairwise distance on the data in that it improves the classification using the nearest neighbour classifier by both reducing the number of other-class items in same-class clusters, and increasing the pairwise distance contrast. Compared to the state of the art in fingerprinting localisation indoors, our contribution is that we successfully address the unsupervised domain adaptation problem.Finally, we raise the hypothesis that some knowledge can be shared between learners in different houses in a privacy-preserving manner. We adapt federated learning (FL) to the multi-residence indoor localisation scenario, which has not been done before, and propose a localfine-tuning algorithm with acceptance based on local validation error improvement. We find the tuned FL each client has a better personalised model compared to benchmark FL while keeping learning dynamics smooth for all clients

Deep Grassmann Manifold Optimization for Computer Vision

In this work, we propose methods that advance four areas in the field of computer vision: dimensionality reduction, deep feature embeddings, visual domain adaptation, and deep neural network compression. We combine concepts from the fields of manifold geometry and deep learning to develop cutting edge methods in each of these areas. Each of the methods proposed in this work achieves state-of-the-art results in our experiments. We propose the Proxy Matrix Optimization (PMO) method for optimization over orthogonal matrix manifolds, such as the Grassmann manifold. This optimization technique is designed to be highly flexible enabling it to be leveraged in many situations where traditional manifold optimization methods cannot be used. We first use PMO in the field of dimensionality reduction, where we propose an iterative optimization approach to Principal Component Analysis (PCA) in a framework called Proxy Matrix optimization based PCA (PM-PCA). We also demonstrate how PM-PCA can be used to solve the general $L_p$-PCA problem, a variant of PCA that uses arbitrary fractional norms, which can be more robust to outliers. We then present Cascaded Projection (CaP), a method which uses tensor compression based on PMO, to reduce the number of filters in deep neural networks. This, in turn, reduces the number of computational operations required to process each image with the network. Cascaded Projection is the first end-to-end trainable method for network compression that uses standard backpropagation to learn the optimal tensor compression. In the area of deep feature embeddings, we introduce Deep Euclidean Feature Representations through Adaptation on the Grassmann manifold (DEFRAG), that leverages PMO. The DEFRAG method improves the feature embeddings learned by deep neural networks through the use of auxiliary loss functions and Grassmann manifold optimization. Lastly, in the area of visual domain adaptation, we propose the Manifold-Aligned Label Transfer for Domain Adaptation (MALT-DA) to transfer knowledge from samples in a known domain to an unknown domain based on cross-domain cluster correspondences

Path finding on a spherical self-organizing map using distance transformations

Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible to use these maps to track temporal and state changes within a high-dimensional space. A popular dimensionality reduction method that produces a mapping for these purposes is the Self-Organizing Map. By using its topology preserving capabilities with a colour-based visualization method known as the U-Matrix, state transitions can be visualized as trajectories on the resulting mapping. Through these trajectories, one can gather information on the transition path between two points in the original high-dimensional state space. This raises the interesting question of whether or not the Self-Organizing Map can be used to discover the transition path between two points in an n-dimensional space. In this thesis, we use a spherically structured Self-Organizing Map called the Geodesic Self-Organizing Map for dimensionality reduction and the creation of a topological mapping that approximates the n-dimensional space. We rst present an intuitive method for a user to navigate the surface of the Geodesic SOM. A new application of the distance transformation algorithm is then proposed to compute the path between two points on the surface of the SOM, which corresponds to two points in the data space. Discussions will then follow on how this application could be improved using some form of surface shape analysis. The new approach presented in this thesis would then be evaluated by analyzing the results of using the Geodesic SOM for manifold embedding and by carrying out data analyses using carbon dioxide emissions data

Path finding on a spherical self-organizing map using distance transformations

Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible to use these maps to track temporal and state changes within a high-dimensional space. A popular dimensionality reduction method that produces a mapping for these purposes is the Self-Organizing Map. By using its topology preserving capabilities with a colour-based visualization method known as the U-Matrix, state transitions can be visualized as trajectories on the resulting mapping. Through these trajectories, one can gather information on the transition path between two points in the original high-dimensional state space. This raises the interesting question of whether or not the Self-Organizing Map can be used to discover the transition path between two points in an n-dimensional space. In this thesis, we use a spherically structured Self-Organizing Map called the Geodesic Self-Organizing Map for dimensionality reduction and the creation of a topological mapping that approximates the n-dimensional space. We rst present an intuitive method for a user to navigate the surface of the Geodesic SOM. A new application of the distance transformation algorithm is then proposed to compute the path between two points on the surface of the SOM, which corresponds to two points in the data space. Discussions will then follow on how this application could be improved using some form of surface shape analysis. The new approach presented in this thesis would then be evaluated by analyzing the results of using the Geodesic SOM for manifold embedding and by carrying out data analyses using carbon dioxide emissions data

Distributed hybrid unit quaternion localisation of camera networks

openSeveral dynamical systems evolve on angular type of variables, such as the pose of rigid bodies or optimization techniques applied to variables of unitary norms. Perhaps the most suitable mathematical tool for describing such dynamics corresponds to the n-dimensional sphere, that is the manifold of dimension n embedded in the (n+1) dimensional Euclidean space and corresponding to all the vectors having unit norm. A relevant example corresponds to the 3-sphere and the ensuing quaternion-based coordinate system, which is largely used for describing the pose of rigid bodies. One of the challenges in describing dynamics evolving on the n-dimensional sphere is the fact that global robust stabilization of a point cannot be accomplished with continuous feedback laws. It is then necessary to resort to alternative solutions, for wanting robustness of the closed-loop stability properties. Hybrid dynamical systems are a possible answer to this, where existing works on the distributed calibration of camera networks will be first overviewed, and hybrid solutions will be proposed and tested.Several dynamical systems evolve on angular type of variables, such as the pose of rigid bodies or optimization techniques applied to variables of unitary norms. Perhaps the most suitable mathematical tool for describing such dynamics corresponds to the n-dimensional sphere, that is the manifold of dimension n embedded in the (n+1) dimensional Euclidean space and corresponding to all the vectors having unit norm. A relevant example corresponds to the 3-sphere and the ensuing quaternion-based coordinate system, which is largely used for describing the pose of rigid bodies. One of the challenges in describing dynamics evolving on the n-dimensional sphere is the fact that global robust stabilization of a point cannot be accomplished with continuous feedback laws. It is then necessary to resort to alternative solutions, for wanting robustness of the closed-loop stability properties. Hybrid dynamical systems are a possible answer to this, where existing works on the distributed calibration of camera networks will be first overviewed, and hybrid solutions will be proposed and tested