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Explaining nonlinear classification decisions with deep Taylor decomposition
TLDR
We introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Expand
Methods for interpreting and understanding deep neural networks
TLDR
This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. Expand
Review of the BCI Competition IV
TLDR
The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. Expand
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges
TLDR
In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs. Expand
Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces
TLDR
We propose a simple unsupervised adaptation method of the linear discriminant analysis classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. Expand
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites
TLDR
We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. Expand
Kernel-Based Nonlinear Blind Source Separation
TLDR
We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). Expand
Deep Semi-Supervised Anomaly Detection
TLDR
We present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection, yielding appreciable performance improvements even when provided with little labeled data. Expand
SchNet - A deep learning architecture for molecules and materials.
TLDR
We present the deep learning architecture SchNet which can be applied to a variety of applications ranging from the prediction of chemical properties for diverse datasets of molecules and materials to highly accurate predictions of potential energy surfaces. Expand
Kernel PCA pattern reconstruction via approximate pre-images.
TLDR
Algorithms based on Mercer kernels construct their solutions in terms of expansions in a high-dimensional feature space F can be performed using a kernel without explicitly working in F. Expand
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