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• JOURNAL ARTICLE
  Creswell A, Bharath AA, 2018,

  Denoising Adversarial Autoencoders.

  , IEEE Trans Neural Netw Learn Syst

  Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabeled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabeled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clean input samples from corrupted ones. Representations may be further improved by introducing regularization during training to shape the distribution of the encoded data in the latent space. We suggest denoising adversarial autoencoders (AAEs), which combine denoising and regularization, shaping the distribution of latent space using adversarial training. We introduce a novel analysis that shows how denoising may be incorporated into the training and sampling of AAEs. Experiments are performed to assess the contributions that denoising makes to the learning of representations for classification and sample synthesis. Our results suggest that autoencoders trained using a denoising criterion achieve higher classification performance and can synthesize samples that are more consistent with the input data than those trained without a corruption process.

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• JOURNAL ARTICLE
  Dolan D, Jensen HJ, Mediano PAM, Molina-Solana M, Rajpal H, Rosas F, Sloboda JAet al., 2018,

  The Improvisational State of Mind: A Multidisciplinary Study of an Improvisatory Approach to Classical Music Repertoire Performance

  , FRONTIERS IN PSYCHOLOGY, Vol: 9, ISSN: 1664-1078
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• JOURNAL ARTICLE
  Gomez-Romero J, Molina-Solana M, Oehmichen A, Guo Yet al., 2018,

  Visualizing large knowledge graphs: A performance analysis

  , FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 89, Pages: 224-238, ISSN: 0167-739X
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• JOURNAL ARTICLE
  Gómez-Romero J, Molina-Solana M, Ros M, Ruiz MD, Martin-Bautista MJet al., 2018,

  Comfort as a service: a new paradigm for residential environmental quality control

  , Sustainability, Vol: 10, ISSN: 1937-0709

  This paper introduces the concept of Comfort as a Service (CaaS), a new energy supply paradigm for providing comfort to residential customers. CaaS takes into account the available passive and active elements, the external factors that affect energy consumption and associated costs, and occupants' behaviors to generate optimal control strategies for the domestic equipment automatically. As a consequence, it releases building occupants from operating the equipment, which gives rise to a disruption of the traditional model of paying per consumed energy in favor of a model of paying per provided comfort. In the paper, we envision a realization of CaaS based on several technologies such as ambient intelligence, big data, cloud computing and predictive computing. We discuss the opportunities and the barriers of CaaS-centered business and exemplify the potential of CaaS deployments by quantifying the expected energy savings achieved after limiting occupants' control over the air conditioning system in a test scenario.

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• JOURNAL ARTICLE
  Song J, Fan S, Lin W, Mottet L, Woodward H, Wykes MD, Arcucci R, Xiao D, Debay J-E, ApSimon H, Aristodemou E, Birch D, Carpentieri M, Fang F, Herzog M, Hunt GR, Jones RL, Pain C, Pavlidis D, Robins AG, Short CA, Linden PFet al., 2018,

  Natural ventilation in cities: the implications of fluid mechanics

  , BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218
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  • Citations: 1
• JOURNAL ARTICLE
  Jahani E, Sundsøy P, Bjelland J, Bengtsson L, Pentland AS, de Montjoye Y-Aet al., 2017,

  Improving official statistics in emerging markets using machine learning and mobile phone data

  , EPJ Data Science, Vol: 6
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• JOURNAL ARTICLE
  Molina-Solana M, Birch D, Guo Y-K, 2017,

  Improving data exploration in graphs with fuzzy logic and large-scale visualisation

  , APPLIED SOFT COMPUTING, Vol: 53, Pages: 227-235, ISSN: 1568-4946
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  • Citations: 3
• JOURNAL ARTICLE
  Molina-Solana M, Ros M, Dolores Ruiz M, Gomez-Romero J, Martin-Bautista MJet al., 2017,

  Data science for building energy management: A review

  , RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 70, Pages: 598-609, ISSN: 1364-0321
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  • Citations: 26
• JOURNAL ARTICLE
  Steele JE, Sundsoy PR, Pezzulo C, Alegana VA, Bird TJ, Blumenstock J, Bjelland J, Engo-Monsen K, de Montjoye Y-A, Iqbal AM, Hadiuzzaman KN, Lu X, Wetter E, Tatem AJ, Bengtsson Let al., 2017,

  Mapping poverty using mobile phone and satellite data

  , JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 14, ISSN: 1742-5689
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  • Citations: 7
• CONFERENCE PAPER
  Arulkumaran K, Dilokthanakul N, Shanahan M, Bharath AAet al., 2016,

  Classifying Options for Deep Reinforcement Learning.

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• 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
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  • Citations: 28
• JOURNAL ARTICLE
  Creswell A, Bharath AA, 2016,

  Task Specific Adversarial Cost Function.

  , CoRR, Vol: abs/1609.08661
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• 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
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  • Citations: 1
• JOURNAL ARTICLE
  McGinn D, Birch D, Akroyd D, Molina-Solana M, Guo Y, Knottenbelt WJet al., 2016,

  Visualizing Dynamic Bitcoin Transaction Patterns

  , BIG DATA, Vol: 4, Pages: 109-119, ISSN: 2167-6461
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  • Citations: 9
• JOURNAL ARTICLE
  Taquet M, Quoidbach J, de Montjoye Y-A, Desseilles M, Gross JJet al., 2016,

  Hedonism and the choice of everyday activities

  , Proceedings of the National Academy of Sciences, Vol: 113, Pages: 9769-9773, ISSN: 0027-8424
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