3,645 research outputs found
Learning generative texture models with extended Fields-of-Experts
We evaluate the ability of the popular Field-of-Experts (FoE) to model structure in images. As a test case we focus on modeling synthetic and natural textures. We find that even for modeling single textures, the FoE provides insufficient flexibility to learn good generative models â it does not perform any better than the much simpler Gaussian FoE. We propose an extended version of the FoE (allowing for bimodal potentials) and demonstrate that this novel formulation, when trained with a better approximation of the likelihood gradient, gives rise to a more powerful generative model of specific visual structure that produces significantly better results for the texture task
Energy-based temporal neural networks for imputing missing values
Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset
Automated Image Analysis of Offshore Infrastructure Marine Biofouling
Supplementary Materials: The following are available online at www.mdpi.com/2077-1312/6/1/2/s1 Acknowledgments: This project was funded by the Natural Environmental Research Council (NERC) project No.: NE/N019865/1. The authors would like to thank Melanie Netherway and Don Orr, from our project partner (company requested to remain anonymous) for the provision of survey footage and for supporting the project. In addition, many thanks to Oscar Beijbom, University California Berkley for providing guidance and support to the project. Additional thanks to Calum Reay, Bibby Offshore; George Gair, Subsea 7; and Alan Buchan, Wood Group Kenny for help with footage collection and for allowing us to host workshops with them and their teams, their feedback and insights were very much appreciated.Peer reviewedPublisher PD
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Developing professionalism in new IT graduates? Who needs it?
A new graduate may require a period of âacclimatisationâ through a process of âdeveloping their professionalismâ to fit into their work environment. The e-Skills UK Technology Counts Insights 2010 report suggests that 110,500 new entrants a year are required to fill IT & Telecoms professional job roles, with 20,800 coming from education (predominantly graduate level and higher). However, 43% of recruiters were reporting a lack of suitable candidates for IT & Telecoms posts where growing importance will be placed on relationship management, business process analysis and design, project and programme management. IT & Telecoms professionals are increasingly expected to be multi-skilled, with sophisticated business and interpersonal skills as well as technical competence. As the report also says: âUK growth will continue to be primarily in high-value roles with an increasing need for customer and business-oriented skills as well as sophisticated technical competencies.â
The diverse needs and requirements of the IT sector, as specified by various employer groups and professional bodies including BCS, IET, eSkills, the CBI and the SFIA Foundation, are discussed. According to the CBI, â62% of entrants to the IT sector need to draw on managerial and professional business skills almost immediately.â For organisations to succeed, their IT graduate recruits must supplement their IT skills with managerial and professional business skills. Well considered CPD will ensure that recent graduates can enhance their âacademicâ skills with the necessary work-based skills for the benefit of both themselves and their new employer. The focus of the improvement will balance the student-centred needs for development and the engaging employerâs commercial needs
Comparing Probabilistic Models for Melodic Sequences
Modelling the real world complexity of music is a challenge for machine
learning. We address the task of modeling melodic sequences from the same music
genre. We perform a comparative analysis of two probabilistic models; a
Dirichlet Variable Length Markov Model (Dirichlet-VMM) and a Time Convolutional
Restricted Boltzmann Machine (TC-RBM). We show that the TC-RBM learns
descriptive music features, such as underlying chords and typical melody
transitions and dynamics. We assess the models for future prediction and
compare their performance to a VMM, which is the current state of the art in
melody generation. We show that both models perform significantly better than
the VMM, with the Dirichlet-VMM marginally outperforming the TC-RBM. Finally,
we evaluate the short order statistics of the models, using the
Kullback-Leibler divergence between test sequences and model samples, and show
that our proposed methods match the statistics of the music genre significantly
better than the VMM.Comment: in Proceedings of the ECML-PKDD 2011. Lecture Notes in Computer
Science, vol. 6913, pp. 289-304. Springer (2011
Response of finite-time particle detectors in non-inertial frames and curved spacetime
The response of the Unruh-DeWitt type monopole detectors which were coupled
to the quantum field only for a finite proper time interval is studied for
inertial and accelerated trajectories, in the Minkowski vacuum in (3+1)
dimensions. Such a detector will respond even while on an inertial trajctory
due to the transient effects. Further the response will also depend on the
manner in which the detector is switched on and off. We consider the response
in the case of smooth as well as abrupt switching of the detector. The former
case is achieved with the aid of smooth window functions whose width, ,
determines the effective time scale for which the detector is coupled to the
field. We obtain a general formula for the response of the detector when a
window function is specified, and work out the response in detail for the case
of gaussian and exponential window functions. A detailed discussion of both and limits are given and several
subtlities in the limiting procedure are clarified. The analysis is extended
for detector responses in Schwarzschild and de-Sitter spacetimes in (1+1)
dimensions.Comment: 29 pages, normal TeX, figures appended as postscript file, IUCAA
Preprint # 23/9
Instantiating deformable models with a neural net
Deformable models are an attractive approach to recognizing objects which have considerable within-class variability such as handwritten characters. However, there are severe search problems associated with fitting the models to data which could be reduced if a better starting point for the search were available. We show that by training a neural network to predict how a deformable model should be instantiated from an input image, such improved starting points can be obtained. This method has been implemented for a system that recognizes handwritten digits using deformable models, and the results show that the search time can be significantly reduced without compromising recognition performance. © 1997 Academic Press
Using generative models for handwritten digit recognition
We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques
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