pietrobarbiero / logic_explained_networksLinks
Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.
☆51Updated 2 years ago
Alternatives and similar repositories for logic_explained_networks
Users that are interested in logic_explained_networks are comparing it to the libraries listed below
Sorting:
- PyTorch Explain: Interpretable Deep Learning in Python.☆161Updated last year
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆54Updated 3 years ago
- Codebase for VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming☆21Updated 2 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆41Updated 2 years ago
- ☆48Updated last year
- ☆65Updated last year
- Code for gradient rollback, which explains predictions of neural matrix factorization models, as for example used for knowledge base comp…☆21Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆58Updated 4 years ago
- Updated code base for GlanceNets: Interpretable, Leak-proof Concept-based models☆25Updated 2 years ago
- Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation☆67Updated 3 years ago
- ZeroC is a neuro-symbolic method that trained with elementary visual concepts and relations, can zero-shot recognize and acquire more com…☆32Updated 2 years ago
- Conditional Theorem Proving☆53Updated 4 years ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- This repository holds the code for the NeurIPS 2022 paper, Semantic Probabilistic Layers☆30Updated last year
- ☆10Updated 4 years ago
- Code for Neural Execution Engines: Learning to Execute Subroutines☆17Updated 4 years ago
- Code accompanying paper: Meta-Learning to Improve Pre-Training☆37Updated 3 years ago
- AutoML Two-Sample Test☆20Updated 3 years ago
- Active and Sample-Efficient Model Evaluation☆24Updated 3 months ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.☆43Updated 6 months ago
- A Scalable Approximate Method for Probabilistic Neurosymbolic Inference☆16Updated 7 months ago
- Official code repository to the corresponding paper.☆29Updated 2 years ago
- EMNLP 2020: On the Ability and Limitations of Transformers to Recognize Formal Languages☆24Updated 4 years ago
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆63Updated 5 years ago
- Self-Explaining Neural Networks☆42Updated 5 years ago
- ModelDiff: A Framework for Comparing Learning Algorithms☆59Updated 2 years ago
- Pytorch implementation of VAEs for heterogeneous likelihoods.☆42Updated 2 years ago
- A simple algorithm to identify and correct for label shift.☆22Updated 7 years ago
- Code for "Neural causal learning from unknown interventions"☆104Updated 5 years ago