Multi-View Bayesian Generative Model for Multi-Subject FMRI Data on Brain Decoding of Viewed Image Categories
暂无分享,去创建一个
Miki Haseyama | Takahiro Ogawa | Ryosuke Harakawa | Yusuke Akamatsu | M. Haseyama | Takahiro Ogawa | Ryosuke Harakawa | Yusuke Akamatsu
[1] Christiane Fellbaum,et al. Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.
[2] Tomoyasu Horikawa,et al. Generic decoding of seen and imagined objects using hierarchical visual features , 2015, Nature Communications.
[3] Samuel Kaski,et al. Group Factor Analysis , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[4] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[5] Miki Haseyama,et al. Estimating Viewed Image Categories from Human Brain Activity via Semi-supervised Fuzzy Discriminative Canonical Correlation Analysis , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[6] Changde Du,et al. Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models , 2019, Engineering.
[7] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[8] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[9] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[10] Hagai Attias,et al. Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.
[11] Jack L. Gallant,et al. Encoding and decoding in fMRI , 2011, NeuroImage.
[12] David D. Cox,et al. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.
[13] J. Gallant,et al. Identifying natural images from human brain activity , 2008, Nature.
[14] Bryan R. Conroy,et al. A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex , 2011, Neuron.
[15] Daoqiang Zhang,et al. Deep Hyperalignment , 2017, NIPS.
[16] Miki Haseyama,et al. Estimation of Viewed Image Categories via CCA Using Human Brain Activity , 2018, 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE).
[17] Daoqiang Zhang,et al. Local Discriminant Hyperalignment for multi-subject fMRI data alignment , 2017, AAAI.
[18] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[19] Anastasios Tefas,et al. Visual representation decoding from human brain activity using machine learning: A baseline study , 2019, Pattern Recognit. Lett..
[20] H. Hotelling. Relations Between Two Sets of Variates , 1936 .
[21] Tomoyasu Horikawa,et al. Characterization of deep neural network features by decodability from human brain activity , 2019, Scientific Data.
[22] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[23] Shinji Nishimoto,et al. Decoding naturalistic experiences from human brain activity via distributed representations of words , 2017, NeuroImage.
[24] Thomas Serre,et al. Reading the mind's eye: Decoding category information during mental imagery , 2010, NeuroImage.
[25] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[26] Po-Hsuan Chen,et al. A Reduced-Dimension fMRI Shared Response Model , 2015, NIPS.
[27] Masa-aki Sato,et al. Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.
[28] Yukiyasu Kamitani,et al. Modular Encoding and Decoding Models Derived from Bayesian Canonical Correlation Analysis , 2013, Neural Computation.
[29] Marcel A. J. van Gerven,et al. Semantic vector space models predict neural responses to complex visual stimuli , 2015, 1510.04738.
[30] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.