clips / interpret_with_rulesLinks
Code for the paper "Rule induction for global explanation of trained models"
☆21Updated 10 months ago
Alternatives and similar repositories for interpret_with_rules
Users that are interested in interpret_with_rules are comparing it to the libraries listed below
Sorting:
- Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net…☆50Updated 2 years ago
- This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching"☆19Updated 3 years ago
- Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"☆45Updated last year
- Code for paper "When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data"☆14Updated 4 years ago
- Code for reproducing experiments in our ACL 2019 paper "Probing Neural Network Comprehension of Natural Language Arguments"☆53Updated 2 years ago
- ☆14Updated 6 years ago
- Code accompanying our paper at AISTATS 2020☆21Updated 4 years ago
- Zero-Shot Open Entity Typing as Type-Compatible Grounding, EMNLP'18.☆42Updated 5 years ago
- Code for paper "Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals"☆18Updated 2 years ago
- Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick T., Hovy E. AAAI 20…☆52Updated 5 years ago
- Code for gradient rollback, which explains predictions of neural matrix factorization models, as for example used for knowledge base comp…☆21Updated 4 years ago
- Code for hyperboloid embeddings for knowledge graph entities☆38Updated 2 years ago
- Layerwise Relevance Visualization in Convolutional Text Graph Classifiers☆12Updated 4 years ago
- Source code for "Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models", ICLR 2020.☆30Updated 4 years ago
- Code for NeurIPS 2019 paper "Hierarchical Optimal Transport for Document Representation"☆54Updated 5 years ago
- Code for EMNLP 2019 paper "Attention is not not Explanation"☆58Updated 4 years ago
- Pytorch implementation of DiffMask☆55Updated last year
- Explaining neural decisions contrastively to alternative decisions.☆25Updated 4 years ago
- Interpretable Neural Predictions with Differentiable Binary Variables☆84Updated 4 years ago
- ☆24Updated 4 years ago
- Frame-Semantic and PropBank Semantic Role Labeling with Syntactic Scaffolding.☆50Updated 3 years ago
- Making a bridge between NLP models and Brain data☆18Updated 5 years ago
- TextGraphs-13 Shared Task on Multi-Hop Inference Explanation Regeneration☆44Updated 5 years ago
- Relevant code for the "Show Your Work" paper, EMNLP 2019.☆18Updated 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
- A Diagnostic Study of Explainability Techniques for Text Classification☆67Updated 4 years ago
- Code and Supplementary Material to the Paper: Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysi…☆36Updated last year
- Code for paper "Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging"☆16Updated 6 years ago
- GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition☆31Updated 3 years ago
- Batch IS NOT Heavy: Learning Word Representations From All Samples☆10Updated 6 years ago