Haut PDF Learning representations from functional MRI data
Learning representations from functional MRI data
... of functional brain-imaging technologies, cog- nitive neuroscience is accumulating maps of neural activity responses to specific tasks or stimuli, or of spontaneous ...consider data from ...
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Operator-valued Kernels for Learning from Functional Response Data
... of functional data is how one can find an appropriate space and a basis in which the functions can be decomposed in a computationally feasible way while taking into account the functional nature of ...
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Learning from genomic data : efficient representations and algorithms.
... The representations learned by these deep convolutional architectures were shown to capture concepts such as edges or textures in the first layers, and objects or parts of objects such as eyes or cats in deeper ...
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Non-invasive inference of information flow using diffusion MRI, functional MRI, and MEG
... HCP data showed that the solutions found by using func- tional and diffusion MRI identify fewer cortical regions while still explaining the M/EEG ...activity from MEG measurements is an ill-posed ...
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Kernel-based learning on hierarchical image representations : applications to remote sensing data classification
... Figure 3.18: Classification accuracy w.r.t. different maximum considered subpath lengths P. SBoSK is computed on the Strasbourg Spot-4 image with D = 4096. known techniques for spatial/spectral remote sensing image ...
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Learning representations for Information Retrieval
... tional representations of documents and ...useful representations for documents and queries has always been central to information retrieval ...such representations and the relevance relationship ...
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Learning from ranking data : theory and methods
... the data. However, summarizing ranking variability is far from straightforward and extending simple concepts such as that of an average or median in the context of preference data, ...far from ...
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Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training
... multimodal MRI and the true [ 11 C]PIB PET DVR parametric ...multimodal MRI and the myelin content in ...multimodal MRI data is complex, and only two layers are not powerful enough to ...
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Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps
... depression from the pre-existing cohort ...T1 MRI were trained to distinguish: LOAD vs Depression, FTD vs LOAD, EOAD vs Depression, EOAD vs ...tridimensional representations of discriminant atrophy ...
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Learning Disentangled Representations via Mutual Information Estimation
... Deep learning success involves supervised learning where massive amounts of labeled data are used to learn useful representations from raw ...labeled data is not always ...
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Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
... Deep Learning Representations of GAN-data Behave as Gaussian Mixtures tors and thus an appropriate statistical model of realistic ...these representations is the same as on a GMM model with ...
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A shape base framework to segmentation of tongue contours from MRI data
... We briefly review previous work we believe to be the most relevant to the presented method. [1] proposed a popu- lar model that has been frequently used in speech processing. A PCA-guided articulatory model is built to ...
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Reproducible evaluation of Alzheimer's Disease classification from MRI and PET data
... machine learning paper? Paving the way towards fully-reproducible research on classification of Alzheimer’s disease’, Machine Learning in Medical Imaging, MLMI 2017, LNCS, ...
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Learning Multicriteria Fuzzy Classification Method PROAFTN from Data
... j (b h i ), j = 1,2..,m; h=1,2,. . . ,k and i= 1,2,. . . , Lh. When evaluating a certain quantity or a measure with a regular (crisp) inter- val, there are two extreme cases, which we should try to avoid. It is possible ...
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Frankenstein: Learning Deep Face Representations using Small Data
... metric learning achieve promising performance for uncontrolled face recognition, it remains cumbersome to improve the design of hand-crafted local features (such as SIFT [25]) and their aggregation mechanisms ...
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Using partial correlation to enhance structural equation modeling of functional MRI data.
... The relationships between structural equation modeling and conditional cor- relation have been the topic of much research and involve graph theoretic con- cepts like morality and d-separation (Whittaker, 1990; Lauritzen, ...
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Fuzzy Rule Learning for Material Classification from Imprecise Data
... requiring learning or parameteriza- tion since it relies on the manual selection of a ...classical representations that have been used in conjunction with this ...
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Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
... periodic blocked design. However, this interleaved partial k -space sampling cannot be exploited in aperiodic dynamic acquisition schemes like in resting state fMRI (rs-fMRI) or during fast-event related fMRI paradigms ...
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Role of homeostasis in learning sparse representations.
... Perrinet Institut de Neurosciences de la Timone UMR7289 CNRS / Aix-Marseille Universit´e — France e-mail: Laurent.Perrinet@univ-amu.fr http://invibe.net/LaurentPerrinet/Publications/Perr[r] ...
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Learning Obstacle Representations for Neural Motion Planning
... representation learning 1 Introduction Motion planning is a fundamental robotics problem [ 2 , 3 ] with numerous applications in mo- bile robot navigation [ 4 ], industrial robotics [ 5 ], humanoid robotics [ 6 ] ...
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