Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • JOURNAL ARTICLE
    Creswell A, Bharath AA,

    Task Specific Adversarial Cost Function

    The cost function used to train a generative model should fit the purpose ofthe model. If the model is intended for tasks such as generating perceptuallycorrect samples, it is beneficial to maximise the likelihood of a sample drawnfrom the model, Q, coming from the same distribution as the training data, P.This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q||P].However, if the model is intended for tasks such as retrieval or classificationit is beneficial to maximise the likelihood that a sample drawn from thetraining data is captured by the model, equivalent to minimising KL[P||Q]. Thecost function used in adversarial training optimises the Jensen-Shannon entropywhich can be seen as an even interpolation between KL[Q||P] and KL[P||Q]. Here,we propose an alternative adversarial cost function which allows easy tuning ofthe model for either task. Our task specific cost function is evaluated on adataset of hand-written characters in the following tasks: Generation,retrieval and one-shot learning.

  • JOURNAL ARTICLE
    Creswell A, Bharath AA,

    Denoising Adversarial Autoencoders

    Unsupervised learning is of growing interest because it unlocks the potentialheld in vast amounts of unlabelled data to learn useful representations forinference. Autoencoders, a form of generative model, may be trained by learningto reconstruct unlabelled input data from a latent representation space. Morerobust representations may be produced by an autoencoder if it learns torecover clean input samples from corrupted ones. Representations may be furtherimproved by introducing regularisation during training to shape thedistribution of the encoded data in latent space. We suggest denoisingadversarial autoencoders, which combine denoising and regularisation, shapingthe distribution of latent space using adversarial training. We introduce anovel analysis that shows how denoising may be incorporated into the trainingand sampling of adversarial autoencoders. Experiments are performed to assessthe contributions that denoising makes to the learning of representations forclassification and sample synthesis. Our results suggest that autoencoderstrained using a denoising criterion achieve higher classification performance,and can synthesise samples that are more consistent with the input data thanthose trained without a corruption process.

  • JOURNAL ARTICLE
    Curcin V, Guo Y, Gilardoni F,

    Scientific Workflow Applied to Nano-and Material Sciences

  • CONFERENCE PAPER
    Arulkumaran K, Dilokthanakul N, Shanahan M, Bharath AAet al., 2016,

    Classifying Options for Deep Reinforcement Learning.

  • JOURNAL ARTICLE
    Bertone G, Calore F, Caron S, Ruiz R, Kim JS, Trotta R, Weniger Cet al., 2016,

    Global analysis of the pMSSM in light of the Fermi GeV excess: prospects for the LHC Run-II and astroparticle experiments

    , JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, ISSN: 1475-7516
  • JOURNAL ARTICLE
    Ma Z-B, Yang Y, Liu Y-X, Bharath AAet al., 2016,

    Recurrently Decomposable 2-D Convolvers for FPGA-Based Digital Image Processing

    , IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 63, Pages: 979-983, ISSN: 1549-7747
  • CONFERENCE PAPER
    Heinis T, Ailamaki A, 2015,

    Reconsolidating Data Structures.

    , Publisher: OpenProceedings.org, Pages: 665-670
  • JOURNAL ARTICLE
    Heinis T, Ham DA, 2015,

    On-the-Fly Data Synopses: Efficient Data Exploration in the Simulation Sciences

    , SIGMOD RECORD, Vol: 44, Pages: 23-28, ISSN: 0163-5808
  • CONFERENCE PAPER
    Karpathiotakis M, Alagiannis I, Heinis T, Branco M, Ailamaki Aet al., 2015,

    Just-In-Time Data Virtualization: Lightweight Data Management with ViDa.

    , Publisher: www.cidrdb.org
  • CONFERENCE PAPER
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Associating Locations Between Indoor Journeys from Wearable Cameras

    , 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743
  • JOURNAL ARTICLE
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Appearance-based indoor localization: A comparison of patch descriptor performance

    , PATTERN RECOGNITION LETTERS, Vol: 66, Pages: 109-117, ISSN: 0167-8655
  • CONFERENCE PAPER
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Indoor Localisation with Regression Networks and Place Cell Models.

    , Publisher: BMVA Press, Pages: 147.1-147.1
  • CONFERENCE PAPER
    Tauheed F, Heinis T, Ailamaki A, 2015,

    THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads.

    , Publisher: ACM, Pages: 939-950
  • JOURNAL ARTICLE
    Guo Y, He S, Guo L, 2014,

    Enhancing Cloud Resource Utilisation using Statistical Analysis

    Resource provisioning based on virtual machine (VM) has been widely accepted and adopted in cloud computing environments. A key problem resulting from using static scheduling approaches for allocating VMs on different physical machines (PMs) is that resources tend to be not fully utilised. Although some existing cloud reconfiguration algorithms have been developed to address the problem, they normally result in high migration costs and low resource utilisation due to ignoring the multi-dimensional characteristics of VMs and PMs. In this paper we present and evaluate a new algorithm for improving resource utilisation for cloud providers. By using a multivariate probabilistic model, our algorithm selects suitable PMs for VM re-allocation which are then used to generate a reconfiguration plan. We also describe two heuristics metrics which can be used in the algorithm to capture the multi-dimensional characteristics of VMs and PMs. By combining these two heuristics metrics in our experiments, we observed that our approach improves the resource utilisation level by around 8% for cloud providers, such as IC Cloud, which accept user-defined VM configurations and 14% for providers, such as Amazon EC2, which only provide limited types of VM configurations.

  • JOURNAL ARTICLE
    Han R, Ghanem MM, Guo L, Guo Y, Osmond Met al., 2014,

    Enabling cost-aware and adaptive elasticity of multi-tier cloud applications

    , FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 32, Pages: 82-98, ISSN: 0167-739X

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