In this work, we attempt to explain the prediction of any black-box classifier from an information-theoretic perspective. For this purpose, we propose two attribution maps: an information gain (IG) map and a point-wise mutual information (PMI) map… IG map provides a class-independent answer to “How informative is each pixel? “, and PMI map offers a class-specific explanation by answering “How much does each pixel support a specific class?” In this manner, we propose (i) a theory-backed attribution method. The attribution […]

While existing predictive frameworks are able to handle Euclidean structured data (i.e, brain images), they might fail to generalize to geometric non-Euclidean data such as brain networks. Besides, these are rooted the sample selection step in using Euclidean or learned similarity measure between vectorized training and testing brain networks… Such sample connectomic representation might include irrelevant and redundant features that could mislead the training sample selection step. Undoubtedly, this fails to exploit and preserve the topology of the brain connectome. […]

ANNdotNET is an open source project for deep learning written in C# with ability to create, train, evaluate and export deep learning models. The project consists of the Graphical User Interface module capable to visually prepare data, fine tune hyper-parameters, design network architecture, evaluate and test trained models… The ANNdotNET introduces the Visual Network Designer, (VND) for visually design almost any sequential deep learning network. Beside VND, ANNdotNET implements Machine Learning Engine, (MLE) based on CNTK – deep learning framework, […]