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Functional Magnetic Resonance Imaging (fMRI) data consists of time series for each voxel recorded during a cognitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cognitive task. We propose a simple and efficient algorithm for decoding the… (More)

- Orhan Firat, Emre Aksan, Ilke Öztekin, Fatos T. Yarman-Vural
- MLMMI@ICML
- 2015

- Itir Önal, Emre Aksan, +4 authors Fatos T. Yarman-Vural
- ICPR
- 2014

An information theoretic approach is proposed to estimate the degree of connectivity for each voxel with its neighboring voxels. The neighborhood system is defined by spatial and functional connectivity metrics. Then, a local mesh of variable size is formed around each voxel using spatial or functional neighborhood. The mesh arc weights, called Mesh Arc… (More)

- Itir Önal, Emre Aksan, +4 authors Fatos T. Yarman-Vural
- 2014 22nd Signal Processing and Communications…
- 2014

In this study, the degree of connectivity for each voxel, which is the unit element of functional Magnetic Resonance Imaging (fMRI) data, with its neighboring voxels is estimated. The neighborhood system is defined by spatial connectivity metrics and a local mesh of variable size is formed around each voxel using spatial neighborhood. Then, the mesh arc… (More)

- Adrian Spurr, Emre Aksan, Otmar Hilliges
- ArXiv
- 2017

In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information… (More)

- Burak Velioglu, Emre Aksan, Itir Önal, Orhan Firat, Mete Ozay, Fatos T. Yarman-Vural
- 2014 IEEE 13th International Conference on…
- 2014

In this study, we propose a new approach to construct a two-level functional brain network. The nodes of the first-level network are the voxels of the functional Magnetic Resonance Images (fMRI) recorded during an object recognition task. The nodes of the network at the second-level are the anatomic regions of the brain. The arcs of the first level are… (More)

- Partha Ghosh, Jie Song, Emre Aksan, Otmar Hilliges
- ArXiv
- 2017

We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural… (More)

- Itir Onal, Emre Aksan, +4 authors Fatos T.Yarman Vural
- 2014

In this study, the degree of connectivity for each voxel, which is the unit element of functional Magnetic Resonance Imaging (fMRI) data, with its neighboring voxels is estimated. The neighborhood system is defined by spatial connectivity metrics and a local mesh of variable size is formed around each voxel using spatial neighborhood. Then, the mesh arc… (More)

- Itir Onal, Emre Aksan, Burak Velioglu, Orhan Firat, Mete Ozay, Fatos T. Yarman Vural
- 2015 23nd Signal Processing and Communications…
- 2015

We suggest a new approach to estimate a brain network to model cognitive tasks and explore the node degree distribution of this network in anatomic regions. Functional Magnetic Resonance Images are used to estimate the relationship among the voxels. First, a local mesh is formed around each voxel in a predefined neighborhood system. Then, the edge weights… (More)

- Orhan Firat, Emre Aksan, Ilke Oztekin, Fatos T. Yarman Vural
- ArXiv
- 2014

Functional magnetic resonance imaging produces high dimensional data, with a less then ideal number of labelled samples for brain decoding tasks (predicting brain states). In this study, we propose a new deep temporal convolutional neural network architecture with spatial pooling for brain decoding which aims to reduce dimensionality of feature space along… (More)

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