dtak / tree-regularization-publicLinks
Code for AAAI 2018 accepted paper: "Beyond Sparsity: Tree Regularization of Deep Models for Interpretability"
☆78Updated 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:
- ☆124Updated 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 7 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆61Updated 6 years ago
- pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"☆301Updated 6 years ago
- Adaptive Neural Trees☆155Updated 6 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 4 years ago
- Gradient based hyperparameter optimization & meta-learning package for TensorFlow☆189Updated 5 years ago
- a python implementation of various versions of the information bottleneck, including automated parameter searching☆128Updated 5 years ago
- I collected some papers about interpretable CNN and reorganized them here.☆131Updated 7 years ago
- ☆134Updated 6 years ago
- ☆43Updated 6 years ago
- Deep Neural Decision Trees☆161Updated 3 years ago
- Code for Deep Bayesian Active Learning (ICML 2017)☆112Updated 7 years ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Updated last year
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- Active Learning on Image Data using Bayesian ConvNets☆139Updated 8 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆100Updated 6 years ago
- Gold Loss Correction☆87Updated 6 years ago
- Computing various norms/measures on over-parametrized neural networks☆49Updated 6 years ago
- Related materials for robust and explainable machine learning☆48Updated 7 years ago
- Learning kernels to maximize the power of MMD tests☆210Updated 7 years ago
- Implementation of Conditionally Shifted Neurons by Munkhdalai et al. (https://arxiv.org/pdf/1712.09926.pdf)☆28Updated 7 years ago
- An implementation of the Deep Neural Decision Forests in PyTorch☆165Updated 6 years ago
- Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE☆40Updated 2 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆128Updated 4 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective t…☆177Updated 2 years ago
- This project contains code for paper Ksenia Konyushkova, Raphael Sznitman, Pascal Fua 'Learning Active Learning from Data', NIPS 2017☆88Updated 3 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆131Updated 5 years ago
- ☆13Updated 7 years ago