dtak / tree-regularization-publicLinks
Code for AAAI 2018 accepted paper: "Beyond Sparsity: Tree Regularization of Deep Models for Interpretability"
☆79Updated 7 years ago
Alternatives and similar repositories for tree-regularization-public
Users that are interested in tree-regularization-public are comparing it to the libraries listed below
Sorting:
- ☆125Updated 4 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆76Updated 8 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆62Updated 6 years ago
- pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"☆302Updated 6 years ago
- Gradient based hyperparameter optimization & meta-learning package for TensorFlow☆190Updated 5 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 4 years ago
- a python implementation of various versions of the information bottleneck, including automated parameter searching☆130Updated 5 years ago
- ☆135Updated 6 years ago
- Code for "Neural causal learning from unknown interventions"☆104Updated 5 years ago
- GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon …☆21Updated 4 years ago
- Adaptive Neural Trees☆155Updated 6 years ago
- On the decision boundary of deep neural networks☆38Updated 7 years ago
- Feature Interaction Interpretability via Interaction Detection☆35Updated 2 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 3 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆100Updated 6 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- ☆65Updated last year
- Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"☆127Updated 6 years ago
- Code for our paper "Sparse Attentive Backtracking: Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding" https://p…☆39Updated 6 years ago
- pytorch neural network attention mechanism☆148Updated 6 years ago
- Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Ba…☆31Updated 4 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 4 years ago
- Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018☆22Updated 6 years ago
- Gold Loss Correction☆88Updated 7 years ago
- Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"☆28Updated 4 years ago
- I collected some papers about interpretable CNN and reorganized them here.☆132Updated 7 years ago
- ☆146Updated 8 years ago
- Supervised Local Modeling for Interpretability☆29Updated 7 years ago
- ☆43Updated 7 years ago
- Code for Deep Bayesian Active Learning (ICML 2017)☆112Updated 7 years ago