349 citations · 763 across the 12 of their papers we have counts for
12 papers
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition
Zhen Liang, Rushuang Zhou, Li Zhang +4
How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic…
Optimization and Validation of Diffusion MRI-based Fiber Tracking with Neural Tracer Data as a Reference
Carlos Enrique Gutierrez, Henrik Skibbe, Ken Nakae +11
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network…
MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography
Henrik Skibbe, Akiya Watakabe, Ken Nakae +10
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information. In the Brain/MINDS project, a connectivity study on…
Efficient Diverse Ensemble for Discriminative Co-Tracking
Kourosh Meshgi, Shigeyuki Oba, Shin Ishii
Ensemble discriminative tracking utilizes a committee of classifiers, to label data samples, which are in turn, used for retraining the tracker to localize the target using the col…
Neural Sequence Model Training via -divergence Minimization
Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura +2
We propose a new neural sequence model training method in which the objective function is defined by -divergence. We demonstrate that the objective function generalizes the maxi…
Active Collaborative Ensemble Tracking
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba +1
A discriminative ensemble tracker employs multiple classifiers, each of which casts a vote on all of the obtained samples. The votes are then aggregated in an attempt to localize t…
Efficient Version-Space Reduction for Visual Tracking
Kourosh Meshgi, Shigeyuki Oba, Shin Ishii
Discrminative trackers, employ a classification approach to separate the target from its background. To cope with variations of the target shape and appearance, the classifier is u…
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama +1
We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial l…
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba +1
Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects. They treat the tracking problem as a classification task and use online learning techniques to…
Distributional Smoothing with Virtual Adversarial Training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama +2
We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model…
Deep learning of fMRI big data: a novel approach to subject-transfer decoding
Sotetsu Koyamada, Yumi Shikauchi, Ken Nakae +2
As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these appli…
Principal Sensitivity Analysis
Sotetsu Koyamada, Masanori Koyama, Ken Nakae +1
We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, w…