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Found 16 papers, 2 papers with code
Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation
The noise added to the original image is defined as the gradient of the cost function of the model.
Just Noticeable Difference for Machines to Generate Adversarial Images
We define Just Noticeable Difference for a machine learning model and generate a least perceptible difference for adversarial images which can trick a model.
EEG Classification based on Image Configuration in Social Anxiety Disorder
The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration.
On the Brain Networks of Complex Problem Solving
The network properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions.
Modeling Brain Networks with Artificial Neural Networks
We observe that both undirected and directed brain networks surpass the performances of the network models used in the fMRI literature.
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions.
Energy Saving Additive Neural Network
This "product" is used to construct a vector product in $R^N$.
Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification
In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods.
Zero-Resource Translation with Multi-Lingual Neural Machine Translation
In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation.
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding
The corresponding cognitive process, encoded in the brain, is then represented by these meshes each of which is estimated assuming a linear relationship among the voxel time series in a predefined locality.
Machine Learning Methods for Attack Detection in the Smart Grid
The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods.
Fusion of Image Segmentation Algorithms using Consensus Clustering
A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image.
Learning Deep Temporal Representations for Brain Decoding
By employing the proposed temporal convolutional architecture with spatial pooling, raw input fMRI data is mapped to a non-linear, highly-expressive and low-dimensional feature space where the final classification is conducted.
Discriminative Functional Connectivity Measures for Brain Decoding
The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories.
Mesh Learning for Classifying Cognitive Processes
The arc weights of each mesh are estimated from the voxel intensity values by least squares method.
A New Fuzzy Stacked Generalization Technique and Analysis of its Performance
In this study, a new Stacked Generalization technique called Fuzzy Stacked Generalization (FSG) is proposed to minimize the difference between N -sample and large-sample classification error of the Nearest Neighbor classifier.
