Structured Sparse Modelling with Hierarchical GP

In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data.Comment: SPARS 201

Sparse Machine Learning Methods for Autonomous Decision Making

Sparse regression methods are used for the reconstruction of compressed signals, that are usually sparse in some bases; or in feature selection problem, where only few features are meaningful. This thesis overviews the existing Bayesian methods for dealing with sparsity, improves them and provides new models for these problems. The novel models decrease complexity, allow to model structure and provide uncertainty distributions in such applications as medicine and computer vision. The thesis starts with exploring Bayesian sparsity for the problem of compressive back- ground subtraction. Sparsity naturally arises in this problem as foreground usually occupies only small part of the video frame. The use of Bayesian compressive sensing improves the solutions in independent and multi-task scenarios. It also raises an important problem of exploring the structure of the data, as foreground pixels are usually clustered in groups. The problem of structure modelling in sparse problems is addressed with hierarchical Gaussian processes, that are the Bayesian way of imposing structure without specifying its exact patterns. Full Bayesian inference based on expectation propagation is provided for offline and online algorithms. The experiments demonstrate the applicability of these methods for the compressed background subtraction and brain activity localisation problems. The majority of sparse Bayesian methods are computationally intensive. This thesis proposes a novel sparse regression method based on the Bayesian neural networks. It makes the prediction operation fast and additionally estimates the uncertainty of predictions, while requiring a longer training phase. The results are demonstrated in the active learning scenario, where the estimated uncertainty is used for experiment design. Sparse methods are also used as part of other methods such as Gaussian processes that suffer from high computational complexity. The use of active sparse subsets of data improves the performance on large datasets. The thesis proposes a method of dealing with the complexity problem for online data updates using Bayesian filtering

Disaster mapping from satellites:damage detection with crowdsourced point labels

High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes. Damage mapping at this scale would require hundreds of expert person-hours. However, a combination of crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time. Asking volunteers to place point marks, as opposed to shapes of actual damaged areas, significantly decreases the required analysis time for response during the disaster. However, different volunteers may be inconsistent in their marking. This work presents methods for aggregating potentially inconsistent damage marks to train a neural network damage detector

BCCNet: Bayesian classifier combination neural network

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications.Comment: Presented at NeurIPS 2018 Workshop on Machine Learning for the Developing Worl

Disaster mapping from satellites:damage detection with crowdsourced point labels

High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes. Damage mapping at this scale would require hundreds of expert person-hours. However, a combination of crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time. Asking volunteers to place point marks, as opposed to shapes of actual damaged areas, significantly decreases the required analysis time for response during the disaster. However, different volunteers may be inconsistent in their marking. This work presents methods for aggregating potentially inconsistent damage marks to train a neural network damage detector

Disaster, Infrastructure and Participatory Knowledge:The Planetary Response Network

There are many challenges involved in online participatory humanitarian response. We evaluate the Planetary Response Network (PRN), a collaboration between researchers, humanitarian organizations, and the online citizen science platform Zooniverse. The PRN uses satellite and aerial image analysis to provide stakeholders with high-level situational awareness during and after humanitarian crises. During past deployments, thousands of online volunteers have compared pre- and post-event satellite images to identify damage to infrastructure and buildings, access blockages, and signs of people in distress. In addition to collectively producing aggregated “heat maps” of features that are shared with responders and decision makers, individual volunteers may also flag novel features directly using integrated community discussion software. The online infrastructure facilitates worldwide participation even for geographically focused disasters; this widespread public participation means that high-value information can be delivered rapidly and uniformly even for large-scale crises. We discuss lessons learned from deployments, place the PRN’s distributed online approach in the context of more localized efforts, and identify future needs for the PRN and similar online crisis-mapping projects. The successes of the PRN demonstrate that effective online crisis mapping is possible on a generalized citizen science platform such as the Zooniverse

An Expectation Maximisation Algorithm for Behaviour Analysis in Video

International audienceSurveillance systems require advanced algorithms able to make decisions without a human operator or with minimal assistance from human operators. In this paper we propose a novel approach for dynamic topic modeling to detect abnormal behaviour in video sequences. The topic model de- scribes activities and behaviours in the scene assuming behaviour temporal dynamics. The new inference scheme based on an Expectation-Maximisation algorithm is implemented without an approximation at intermediate stages. The proposed approach for behaviour analysis is compared with a Gibbs sampling inference scheme. The experiments both on synthetic and real data show that the model, based on Expectation-Maximisation approach, outperforms the one, based on Gibbs sampling scheme