alewarne / Layerwise-Relevance-Propagation-for-LSTMs
Tensorflow 2.1 implementation of LRP for LSTMs
☆37Updated 2 years ago
Alternatives and similar repositories for Layerwise-Relevance-Propagation-for-LSTMs:
Users that are interested in Layerwise-Relevance-Propagation-for-LSTMs are comparing it to the libraries listed below
- Layer-wise Relevance Propagation (LRP) for LSTMs.☆223Updated 4 years ago
- Implementation of layer-wise relevance propagation.☆8Updated 6 years ago
- The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Py…☆331Updated 2 years ago
- A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).☆135Updated 4 years ago
- Pytorch implementation of "Exploring Interpretable LSTM Neural Networks over Multi-Variable Data" https://arxiv.org/pdf/1905.12034.pdf☆108Updated 5 years ago
- ☆27Updated last month
- Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification☆95Updated last year
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆82Updated 2 years ago
- ☆17Updated 4 years ago
- ☆90Updated 3 years ago
- Code associated with ACM-CHIL 21 paper 'T-DPSOM - An Interpretable Clustering Method for Unsupervised Learning of Patient Health States'☆68Updated 4 years ago
- ☆34Updated 2 years ago
- Counterfactual Explanations for Multivariate Time Series Data☆31Updated last year
- Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers☆98Updated 6 years ago
- Multivariate recurrent GANs aimed at generating biomedical time-series. Methodology involves drawing symmetries to adversarial image gene…☆24Updated 7 months ago
- ☆100Updated 6 years ago
- Explaining Anomalies Detected by Autoencoders Using SHAP☆40Updated 3 years ago
- Pytorch implementation of various neural network interpretability methods☆116Updated 3 years ago
- A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also in…☆745Updated 4 years ago
- This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its pred…☆74Updated 2 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- GRU-D, a GRU-based model with trainable decays for multivariate time series classification with missing values/irregular samplings☆125Updated 2 years ago
- PyTorch based autoencoder for sequential data☆43Updated 4 years ago
- Contrastive Learning for Time Series☆38Updated last year
- The toolkit to explain Keras model predictions.☆15Updated 7 months ago
- This repository provides details of the experimental code in the paper: Instance-based Counterfactual Explanations for Time Series Classi…☆18Updated 3 years ago
- ☆8Updated last year
- Adapting LIME explanations for Time Series Data☆16Updated 4 months ago
- Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.☆217Updated 8 months ago
- Adversarial Attacks on Deep Neural Networks for Time Series Classification☆76Updated 4 years ago