Add to ORKGThe Layer-wise Relevance Propagation (LRP) algorithm explains a classifier's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself. With the LRP Toolbox we provide platform-agnostic implementations for explaining the predictions of pre-trained state of the art Caffe networks and stand-alone implementations for fully connected Neural Network models. The implementations for Matlab and python shall serve as a playing field to familiarize oneself with the LRP algorithm and are implemented with readability and transparency in mind. Models and data can be imported and exported using raw text formats, Matlab's .mat files and the .npy format f...
Fisher vector (FV) classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms...
Deep learning techniques produce impressive performance in many natural language processing tasks. H...
Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. imag...
Layer-wise relevance propagation (LRP) heatmaps aim to provide graphical explanation for decisions o...
Layer-wise relevance propagation (LRP) heatmaps aim to provide graphical explanation for decisions o...
Layer-wise relevance propagation (LRP) heatmaps aim to provide graphical explanation for decisions o...
This paper provides an entry point to the problem of interpreting a deep neural network model and ex...
Machine learning techniques such as (Deep) Neural Networks are successfully solving a plethora of ta...
Background In cognitive neuroscience the potential of deep neural networks (DNNs) for solving comple...
In this thesis, we consider the problem of neural network (NN) training on imbalanced datasets. The ...
We present the application of layer-wise relevance propagation to several deep neural networks such ...
This thesis is focused on exploring explainable AI algorithms and in particular Layer-Wise Relevance...
In Neural Machine Translation (and, more generally, conditional language modeling), the generation o...
In Neural Machine Translation (and, more generally, conditional language modeling), the generation o...
Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep n...
Fisher vector (FV) classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms...
Deep learning techniques produce impressive performance in many natural language processing tasks. H...
Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. imag...
Layer-wise relevance propagation (LRP) heatmaps aim to provide graphical explanation for decisions o...
Layer-wise relevance propagation (LRP) heatmaps aim to provide graphical explanation for decisions o...
Layer-wise relevance propagation (LRP) heatmaps aim to provide graphical explanation for decisions o...
This paper provides an entry point to the problem of interpreting a deep neural network model and ex...
Machine learning techniques such as (Deep) Neural Networks are successfully solving a plethora of ta...
Background In cognitive neuroscience the potential of deep neural networks (DNNs) for solving comple...
In this thesis, we consider the problem of neural network (NN) training on imbalanced datasets. The ...
We present the application of layer-wise relevance propagation to several deep neural networks such ...
This thesis is focused on exploring explainable AI algorithms and in particular Layer-Wise Relevance...
In Neural Machine Translation (and, more generally, conditional language modeling), the generation o...
In Neural Machine Translation (and, more generally, conditional language modeling), the generation o...
Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep n...
Fisher vector (FV) classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms...
Deep learning techniques produce impressive performance in many natural language processing tasks. H...
Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. imag...