Papers

When available, implementations of the code (generally Matlab) and data are linked next to the corresponding paper below.

• Carreira-Perpiñán, M. Á.: Dimensionality reduction. Book in preparation for Chapman & Hall/CRC Press.

• Wang, W. and Carreira-Perpiñán, M. Á. (2012): "Nonlinear low-dimensional regression using auxiliary coordinates". 15th Int. Workshop on Artificial Intelligence and Statistics (AISTATS 2012), pp. 1295-1304.
  [external link] [paper preprint] [poster] [animations] [Matlab implementation (coming soon)]

• Farhadloo, M. and Carreira-Perpiñán, M. Á. (2012): "Learning and adaptation of a tongue shape model with missing data". IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2012), to appear.
  [external link] [paper preprint] [slides] [Matlab implementation (coming soon)] [© IEEE]

• Farhadloo, M. and Carreira-Perpiñán, M. Á. (2012): "Regularising an adaptation algorithm for tongue shape models". IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2012), to appear.
  [external link] [paper preprint] [poster] [Matlab implementation (coming soon)] [© IEEE]

• Wang, W., Carreira-Perpiñán, M. Á. and Lu, Z. (2011): "A denoising view of matrix completion". Advances in Neural Information Processing Systems 24 (NIPS 2011), pp. 334-342.
  [external link] [paper preprint] [poster] [animations] [Matlab implementation (coming soon)]

• Carreira-Perpiñán, M. Á. and Lu, Z. (2011): "Manifold learning and missing data recovery through unsupervised regression". 12th IEEE Int. Conf. Data Mining (ICDM 2011), pp. 1014-1019.
  [external link] [paper preprint] [slides] [animations] [Matlab implementation (coming soon)] [© IEEE]

• Kamthe, A., Erickson, V., Carreira-Perpiñán, M. Á. and Cerpa, A. E. (2011): "Enabling building energy auditing using adapted occupancy models". 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency In Buildings (BuildSys 2011), to appear.
  [external link] [paper preprint] [slides]

• Kamthe, A., Carreira-Perpiñán, M. Á. and Cerpa, A. E. (2011): "Adaptation of a mixture of multivariate Bernoulli distributions". 22nd Int. Joint Conf. Artificial Intelligence (IJCAI 2011), pp. 1336-1341.
  [external link] [paper preprint] [poster] [Matlab implementation (coming soon)]

• Erickson, V., Carreira-Perpiñán, M. Á. and Cerpa, A. E. (2011): "OBSERVE: Occupancy-Based System for Efficient Reduction of HVAC Energy". 10th Int. Conf. Information Processing in Sensor Networks (IPSN 2011), to appear.
  [external link] [paper preprint] [slides] [poster]

• Carreira-Perpiñán, M. Á. (2011): "Machine learning models of the tongue shape during speech". 9th Int. Seminar on Speech Production (ISSP 2011), pp. 103-110.
  [external link.] [paper preprint] [poster]

• Qin, C., Carreira-Perpiñán, M. Á. and Farhadloo, M. (2010): "Adaptation of a tongue shape model by local feature transformations". Interspeech 2010, pp. 1596-1599.
  [external link] [paper preprint] [poster] [Matlab implementation (coming soon)]

• Qin, C. and Carreira-Perpiñán, M. Á. (2010): "Estimating missing data sequences in X-ray microbeam recordings". Interspeech 2010, pp. 1592-1595.
  [external link] [paper preprint] [poster]
  The preprint fixes a typo in the 3rd eq. of p. 2.

• Qin, C. and Carreira-Perpiñán, M. Á. (2010): "Articulatory inversion of American English /r/ by conditional density modes". Interspeech 2010, pp. 1998-2001.
  [external link] [paper preprint] [slides] [animations]

• Xie, L., Carreira-Perpiñán, M. Á. and Newsam, S. (2010): "Semi-supervised regression with temporal image sequences". IEEE Int. Conf. Image Processing (ICIP 2010), pp. 2637-2640.
  [external link] [paper preprint] [slides] [© IEEE]

• Carreira-Perpiñán, M. Á. (2010): "The elastic embedding algorithm for dimensionality reduction". 27th Int. Conf. Machine Learning (ICML 2010), pp. 167-174.
  [external link] [paper preprint] [slides] [poster] [animations] [Matlab implementation]

• Wang, W. and Carreira-Perpiñán, M. Á. (2010): "Manifold blurring mean shift algorithms for manifold denoising". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2010), pp. 1759-1766.
  [external link] [paper preprint] [slides] [videolectures.net] [poster] [animations] [Matlab implementation (coming soon)] [© IEEE]

• Carreira-Perpiñán, M. Á. and Lu, Z. (2010): "Parametric dimensionality reduction by unsupervised regression". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2010), pp. 1895-1902.
  [external link] [paper preprint] [poster] [animations] [Matlab implementation] [© IEEE]

• Qin, C. and Carreira-Perpiñán, M. Á. (2010): "Reconstructing the full tongue contour from EMA/X-ray microbeam". IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2010), pp. 4190-4193.
  [external link] [paper preprint] [slides] [animations] [Matlab implementation (coming soon)] [© IEEE]

• Massaro, D. A., Carreira-Perpiñán, M. Á. and D. J. Merrill (2010): "An automatic visible speech supplement for deaf individuals' speech comprehension in face-to-face and classroom situations". In Cued Speech and Cued Language Development of Deaf and Hard of Hearing Children (C. LaSasso, K. Lamar Crain and J. Leybaert, eds.), Plural Publishing, ch. 22, pp. 503-530.
  [external link] [paper preprint]

• Qin, C. and Carreira-Perpiñán, M. Á. (2009): "The geometry of the articulatory region that produces a speech sound". Invited paper, 43rd Annual Asilomar Conf. Signals, Systems, and Computers, pp. 1742-1746.
  [external link] [paper preprint] [slides] [© IEEE]

• Qin, C. and Carreira-Perpiñán, M. Á. (2009): "Adaptation of a predictive model of tongue shapes". Interspeech 2009, pp. 772-775.
  [external link] [paper preprint] [poster]

• Kamthe, A., Carreira-Perpiñán, M. Á. and Cerpa A. E. (2009): "M&M: Multi-level Markov model for wireless link simulations". 7th ACM Conf. Embedded Networked Sensor Systems (SenSys 2009), pp. 57-70.
  [external link] [paper preprint] [slides] [poster] [TOSSIM code for TinyOS 2.0]
  Poster Abstract: "Wireless link simulations using multi-level Markov models", pp. 391-392. [external link] [paper preprint]

• Massaro, D. A., Carreira-Perpiñán, M. Á. and D. J. Merrill (2009): "Optimizing visual feature perception for an automatic wearable speech supplement in face-to-face communication and classroom situations". 42st Hawaii Int. Conf. Systems Science (HICSS-42 2009), pp. 1-10.
  [external link] [paper preprint] [© IEEE]

• Massaro, D. A., Carreira-Perpiñán, M. Á., D. J. Merrill, C. Sterling, S. Bigler, E. Piazza and M. Perlman (2008): "iGlasses: an automatic wearable speech supplement in face-to-face communication and classroom situations" (demonstration paper). Int. Conf. Multimodal Interfaces (ICMI 2008), pp. 197-198.
  [external link] [paper preprint]

• Qin, C., Carreira-Perpiñán, M. Á., Richmond, K., Wrench, A. and Renals, S. (2008): "Predicting tongue shapes from a few landmark locations". Interspeech 2008, pp. 2306-2309.
  [external link] [paper preprint] [slides]

• Carreira-Perpiñán, M. Á. and Lu, Z. (2008): "Dimensionality reduction by unsupervised regression". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2008).
  [external link] [paper preprint] [slides] [poster] [animations] [Matlab implementation] [© IEEE]
  The preprint fixes three errata in the legends of fig. 2.

• Carreira-Perpiñán, M. Á. (2008): "Generalised blurring mean-shift algorithms for nonparametric clustering". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2008).
  [external link] [paper preprint] [poster] [animations] [© IEEE]

• Lu, Z. and Carreira-Perpiñán, M. Á. (2008): "Constrained spectral clustering through affinity propagation". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2008).
  [external link] [paper preprint] [poster] [Matlab implementation] [© IEEE]

• Qin, C. and Carreira-Perpiñán, M. Á. (2008): "Trajectory inverse kinematics by conditional density modes". IEEE Int. Conf. on Robotics and Automation (ICRA 2008), pp. 1979-1986.
  [external link] [paper preprint] [slides] [poster] [animations] [© IEEE]

• Qin, C. and Carreira-Perpiñán, M. Á. (2008): "Trajectory inverse kinematics by nonlinear, nongaussian tracking". IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2008), pp. 2057-2060.
  [external link] [paper preprint] [poster] [© IEEE]

• Özertem, U., Erdogmus, D. and Carreira-Perpiñán, M. Á. (2008): "Density geodesics for similarity clustering". IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2008), pp. 1977-1980.
  [external link] [paper preprint] [© IEEE]

• Lu, Z., Carreira-Perpiñán, M. Á. and Sminchisescu, C. (2008): "People tracking with the Laplacian Eigenmaps Latent Variable Model". Advances in Neural Information Processing Systems 20 (NIPS 2007), pp. 1705-1712.
  [external link] [paper preprint] [poster] [animations]
  Preview at The Learning Workshop 2007: [external link] [paper preprint] [poster]

• Qin, C. and Carreira-Perpiñán, M. Á. (2007): "An empirical investigation of the nonuniqueness in the acoustic-to-articulatory mapping". Interspeech 2007, pp. 74-77. Best student paper award.
  [external link] [paper preprint] [slides]

• Qin, C. and Carreira-Perpiñán, M. Á. (2007): "A comparison of acoustic features for articulatory inversion". Interspeech 2007, pp. 2469-2472.
  [external link] [paper preprint] [slides]

• Kazmierczak, S. C., Leen, T. K., Erdogmus, D. and Carreira-Perpiñán, M. Á. (2007): "Reduction of multi-dimensional laboratory data to a two-dimensional plot: a novel technique for the identification of laboratory error". Clinical Chemistry and Laboratory Medicine 45(6):749-752.
  [external link] [paper preprint]

• Myronenko, A., Song, X. and Carreira-Perpiñán, M. Á. (2007): "Free-form nonrigid image registration using generalized elastic nets". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2007).
  [external link] [paper preprint] [poster] [© IEEE]

• Carreira-Perpiñán, M. Á. and Lu, Z. (2007): "The Laplacian Eigenmaps Latent Variable Model". 11th Int. Workshop on Artificial Intelligence and Statistics (AISTATS 2007), pp. 59-66.
  [external link] [paper preprint] [poster] [animations] [Matlab implementation (coming soon)]

• Myronenko, A., Song, X. and Carreira-Perpiñán, M. Á. (2007): "Non-rigid point set registration: Coherent Point Drift". Advances in Neural Information Processing Systems 19 (NIPS 2006), pp. 1009-1016.
  [external link] [paper preprint] [poster] [Matlab implementation]

• Carreira-Perpiñán, M. Á. (2007): "Gaussian mean shift is an EM algorithm". IEEE Trans. on Pattern Analysis and Machine Intelligence 29(5):767-776.
  [external link] [paper preprint] [© IEEE]
  We prove the following properties of the mean-shift algorithm: (1) Mean-shift is an expectation-maximisation (EM) algorithm for Gaussian kernels and a generalised EM algorithm for non-gaussian kernels. (2) Gaussian mean-shift converges linearly to a mode from almost any starting point; the rate of linear convergence approaches 0 (superlinear convergence) for very narrow or very wide kernels, but is often close to 1 (thus extremely slow) for intermediate widths, and exactly 1 (sublinear convergence) for widths at which modes merge. (3) The iterates approach the mode along the local principal component of the data points from the inside of the convex hull of the data points. (4) The convergence domains are nonconvex and can be disconnected and show fractal behaviour.

• Carreira-Perpiñán, M. Á. (2006): "Fast nonparametric clustering with Gaussian blurring mean-shift". 23rd Int. Conf. Machine Learning (ICML 2006), pp. 153-160.
  [external link] [paper preprint] [slides] [poster] [animations] [Java applet] [Matlab implementation]
  We give a stopping criterion and an acceleration for the Gaussian blurring mean-shift algorithm, prove it has cubic convergence rate with Gaussian clusters, connect it with spectral clustering, and show it achieves image segmentations as good as those of Gaussian mean-shift but much faster.

• Carreira-Perpiñán, M. Á. (2006): "Acceleration strategies for Gaussian mean-shift image segmentation". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2006), pp. 1160-1167.
  [external link] [paper preprint] [poster] [Java applet] [Matlab implementation (coming soon)] [© IEEE]
  We attain speedups of one to two orders of magnitude over exact Gaussian mean-shift while achieving essentially the same segmentation, by using techniques based on spatial discretisation, spatial neighbourhoods, sparse EM and EM-Newton algorithms.

• Myronenko, A., Song, X. and Carreira-Perpiñán, M. Á. (2006): "Non-parametric image registration using generalized elastic nets". 9th MICCAI Conference, Int. Workshop on Mathematical Foundations of Computational Anatomy (MFCA 2006), pp. 156-163.
  [external link] [paper preprint]

• Erdogmus, D., Carreira-Perpiñán, M. Á. and Özertem, U. (2006): "Kernel density estimation, affinity-based clustering, and typical cuts". IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'06), vol. 5 pp. 569-572.
  [external link] [paper preprint] [© IEEE]

• Carreira-Perpiñán, M. Á., Dayan, P. and Goodhill, G. J. (2005): "Differential priors for elastic nets". 6th Int. Conf. Intelligent Data Engineering and Automated Learning (IDEAL'05), pp. 335-342, Lecture Notes in Computer Science vol. 3578, Springer-Verlag.
  [external link] [paper preprint] [Matlab implementation] [© Springer-Verlag]

• Carreira-Perpiñán, M. Á. and Hinton, G. E. (2005): "On contrastive divergence learning". 10th Int. Workshop on Artificial Intelligence and Statistics (AISTATS 2005), pp. 59-66, 2005.
  [external link] [paper preprint] [poster]
  Contrastive divergence (CD) is a fast, low-variance Markov chain Monte Carlo method for maximum likelihood estimation of random fields. We show that CD is typically slightly biased and give an unbiased alternative algorithm.

• Carreira-Perpiñán, M. Á. and Zemel, R. S. (2005): "Proximity graphs for clustering and manifold learning". Advances in Neural Information Processing Systems 17 (NIPS 2004), pp. 225-232.
  [external link] [paper preprint] [slides] [poster]
  We propose new types of proximity (neighbourhood) graphs, based on ensembles of minimum spanning trees, for use with (spectral) clustering and manifold learning.

• Carreira-Perpiñán, M. Á., Lister, R. J., and Goodhill, G. J. (2005): "A computational model for the development of multiple maps in primary visual cortex". Cerebral Cortex 15(8):1222-1233.
  [external link] [paper preprint] [Matlab implementation]
  We model the combined development of 5 maps of primary visual cortex (retinotopy, ocular dominance, orientation, direction and spatial frequency) using the elastic net model, as well as the effects of monocular deprivation and single-orientation rearing. We also predict that the stripe width of all maps (OR, DIR, SF) increases slightly under monocular deprivation.

• Carreira-Perpiñán, M. Á. and Goodhill, G. J. (2004): "Influence of lateral connections on the structure of cortical maps". J. Neurophysiology 92(5):2947-2959.
  [external link] [paper preprint] [Matlab implementation]
  Using a generalised elastic net model of cortical maps, we show that the number of excitatory and inhibitory oscillations of a Mexican-hat cortical interaction function has a remarkable effect on the geometric relations between the maps of ocular dominance and orientation. We predict that, in biological maps, this function oscillates only once (central excitation, surround inhibition).

• He, X., Zemel, R. S., and Carreira-Perpiñán, M. Á. (2004): "Multiscale conditional random fields for image labeling". IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2004), pp. 695-702, Washington, DC, 27 June - 2 July 2004.
  [external link] [paper preprint] [Corel and Sowerby datasets (gzipped Matlab format)] [© IEEE]

• Carreira-Perpiñán, M. Á.: "Reconstruction of sequential data with density models". Unpublished manuscript, Jan. 27, 2004, arXiv:1109.3248 [cs.LG].
  [external link] [paper preprint]

• Carreira-Perpiñán, M. Á. and Goodhill, G. J.: "Generalised elastic nets". Unpublished manuscript, Aug. 14, 2003, arXiv:1108.2840 [q-bio.NC].
  [external link] [paper preprint] [Matlab implementation]

• Carreira-Perpiñán, M. Á. and Williams, C. K. I. (2003): An isotropic Gaussian mixture can have more modes than components. Technical report EDI-INF-RR-0185, School of Informatics, University of Edinburgh, UK.
  [external link] [paper] [supplementary information]

• Carreira-Perpiñán, M. Á. and Williams, C. K. I. (2003): "On the number of modes of a Gaussian mixture". Scale-Space Methods in Computer Vision, pp. 625-640, Lecture Notes in Computer Science vol. 2695, Springer-Verlag.
  [external link] [paper preprint] [© Springer-Verlag] [extended technical report version] [supplementary information]

• Carreira-Perpiñán, M. Á. and Williams, C. K. I. (2003): On the number of modes of a Gaussian mixture. Technical report EDI-INF-RR-0159, School of Informatics, University of Edinburgh, UK.
  [external link] [paper] [supplementary information]

• Carreira-Perpiñán, M. Á. and Goodhill, G. J. (2002): "Are visual cortex maps optimized for coverage?". Neural Computation 14(7):1545-1560.
  [external link] [paper preprint]

• Carreira-Perpiñán, M. Á. and Goodhill, G. J. (2002): "Development of columnar structures in primary visual cortex". Invited chapter in Computational Neuroanatomy: Principles and Methods (G. A. Ascoli, ed.), Humana Press, pp. 337-357.
  [external link] [paper preprint]
  This contains a review of data and models for visual cortical maps, with an emphasis on the elastic net.

• Goodhill, G. J. and Carreira-Perpiñán, M. Á. (2002): "Cortical Columns". Invited article in the Encyclopedia of Cognitive Science (L. Nadel, ed.), Macmillan, vol. 1, pp. 845-851.
  [external link] [paper preprint]
  This contains a review of columnar systems in the cortex, in particular in the visual cortex (maps of ocular dominance, orientation, etc.).

• Carreira-Perpiñán, M. Á. and Goodhill, G. J. (2001): "The effect of variable elastic topologies on the structure of ocular dominance and orientation maps". Society for Neuroscience abstracts, 27, 475.21.
  [external link] [poster] [supplementary information]

• Carreira-Perpiñán, M. Á. (2001): Continuous latent variable models for dimensionality reduction and sequential data reconstruction. PhD thesis, University of Sheffield, UK.
  [external link] [abstract] [paper PDF] [paper PS] [list of all references (.tar.gz, BibTeX)]
  The following chapters, available separately, contain tutorial material:

  • Chapter 2: The continuous latent variable modelling formalism.
    This contains a review of continuous latent variable models: probabilistic principal component analysis (PCA), factor analysis, the generative topographic mapping (GTM), independent component analysis (ICA), mixtures of latent variable models, etc. It also deals with issues such as parameter estimation, identifiability, interpretability, visualisation, and dimensionality reduction with continuous latent variable models.
    [paper PDF] [paper PS]
  • Chapter 4: Dimensionality reduction.
    This contains a review of dimensionality reduction with nonprobabilistic methods (probabilistic methods, i.e., latent variable models, are reviewed in chapter 2): nonlinear autoassociators, kernel PCA, principal curves, vector quantisation, multidimensional scaling, Isomap, LLE, etc. It also reviews issues such as the curse of dimensionality and the intrinsic dimensionality.
    [paper PDF] [paper PS]
  • Chapter 6: Inverse problems and mapping inversion.
    This contains a review of inverse problem theory and its methods (Bayesian and nonprobabilistic), and its relation with latent variable models.
    [paper PDF] [paper PS]
  • Chapter 10 (partially): The acoustic-to-articulatory mapping problem.
    This contains a review of the acoustic-to-articulatory mapping problem of speech research (the recovery of the acoustic waveform given the vocal tract configuration), emphasising its potential role in improving automatic speech recognition.
    [paper PDF] [paper PS]

• Carreira-Perpiñán, M. Á. (2000): "Mode-finding for mixtures of Gaussian distributions". IEEE Trans. on Pattern Analysis and Machine Intelligence 22(11):1318-1323.
  [external link] [paper preprint] [© IEEE] [extended technical report version] [Matlab implementation]
  This gives algorithms for finding (nearly) all the maxima of a Gaussian mixture.

• Carreira-Perpiñán, M. Á. (2000): "Reconstruction of sequential data with probabilistic models and continuity constraints". Advances in Neural Information Processing Systems 12 (NIPS'99), pp. 414-420.
  [external link] [paper preprint] [slides]
  This gives a method for reconstructing a sequence of data vectors where some components at some times are missing, with applications to inverse problems in speech (articulatory inversion) and robotics (inverse kinematics).

• Carreira-Perpiñán, M. Á. and Renals, S. (1999): "A latent variable modelling approach to the acoustic-to-articulatory mapping problem". 14th International Congress of Phonetic Sciences (ICPhS'99), pp. 2013-2016, San Francisco, USA, 1-7 August 1999.
  [external link] [paper preprint] [slides]

• Carreira-Perpiñán, M. Á. (1999): "One-to-many mappings, continuity constraints and latent variable models". 1999 IEE Colloquium on Applied Statistical Pattern Recognition, Birmingham, UK.
  [external link] [paper preprint] [slides]

• Carreira-Perpiñán, M. Á. and Renals, S. (1999): "Practical identifiability of finite mixtures of multivariate Bernoulli distributions". Neural Computation 12(1):141-152.
  [external link] [paper preprint] [Matlab implementation]

• Carreira-Perpiñán, M. Á. (1999): Mode-finding for mixtures of Gaussian distributions (revised August 4, 2000). Technical report CS-99-03, Dept. of Computer Science, University of Sheffield, UK.
  [external link] [paper] [supplementary information]

• Carreira-Perpiñán, M. Á. and Renals, S. (1998): "Dimensionality reduction of electropalatographic data using latent variable models". Speech Communication 26(4):259-282.
  [external link] [paper preprint] [supplementary information]

• Carreira-Perpiñán, M. Á. and Renals, S. (1998): "Experimental evaluation of latent variable models for dimensionality reduction". 1998 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing (NNSP98), pp. 165-173, Cambridge, UK.
  [external link] [paper preprint] [© IEEE] [slides]

• Carreira-Perpiñán, M. Á. (1997): Density networks for dimension reduction of continuous data: Analytical solutions. Technical report CS-97-09, Dept. of Computer Science, University of Sheffield, UK.
  [external link] [paper]

• Carreira-Perpiñán, M. Á. (1996): A review of dimension reduction techniques. Technical report CS-96-09, Dept. of Computer Science, University of Sheffield, UK.
  [external link] [paper]
  Note: this document is out of date. A much more extensive review is provided in chapters 2 and 4 of my PhD thesis.

• Carreira-Perpiñán, M. Á. (1995): Compression neural networks and feature extraction: Application to human recognition from ear images (in Spanish). MSc thesis, Technical University of Madrid, Spain.
  [abstract] [paper] [slides] [database of ear images]
  This is probably the first work that introduces the idea of ear biometrics for automatic personal identification.

• Carreira-Perpiñán, M. Á. (1994): "The modelling of the thermal subsystem in spacecraft real-time simulators". 3rd Workshop on Simulators for European Space Programmes, pp. 69-78, ESA/ESTEC, Noordwijk, The Netherlands.
  [abstract] [paper] [slides]

• Carreira-Perpiñán, M. Á. (1994): ESA Thermal Analysis Program (ESATAN): An evaluation. European Space Agency Internal Report DOPS-SST-TN-0215-SIM, Darmstadt, Germany.
  [abstract] [paper]

Lecture notes

• Carreira-Perpiñán, M. Á. (2011): EECS260 Optimization: Lecture notes. University of California, Merced, 2008-2011.
  

• Carreira-Perpiñán, M. Á. (2006): EE 589/689 Foundations of computer vision: Lecture notes. OGI/OHSU, Fall quarter 2006.
  

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