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Interpretability

349 directly classified papers

Papers per year

Papers

Model Distillation for Faithful Explanations of Medical Code Predictions ACL 2022
Task-Agnostic Graph Explanations NIPS 2022
Extending Logic Explained Networks to Text Classification EMNLP 2022
Finding Dataset Shortcuts with Grammar Induction EMNLP 2022
Measuring Context-Word Biases in Lexical Semantic Datasets EMNLP 2022
Optimal Local Explainer Aggregation for Interpretable Prediction AAAI 2022
LIMREF: Local Interpretable Model Agnostic Rule-Based Explanations for Forecasting, with an Application to Electricity Smart Meter Data AAAI 2022
Creating Interpretable Data-Driven Approaches for Tropical Cyclones Forecasting AAAI 2022
Manipulating SHAP via Adversarial Data Perturbations (Student Abstract) AAAI 2022
RES: An Interpretable Replicability Estimation System for Research Publications AAAI 2022
Finding Skill Neurons in Pre-trained Transformer-based Language Models EMNLP 2022
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation EMNLP 2022
On the Symmetries of Deep Learning Models and their Internal Representations NIPS 2022
HINT: Hierarchical Neuron Concept Explainer CVPR 2022
B-Cos Networks: Alignment Is All We Need for Interpretability CVPR 2022
Do Explanations Explain? Model Knows Best CVPR 2022
Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective NIPS 2022
Verification and Repair of Neural Networks AAAI 2021
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation IJCNLP 2021
Towards robust vision by multi-task learning on monkey visual cortex NIPS 2021
Explainable Models with Consistent Interpretations AAAI 2021
A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation AAAI 2021
The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits AAAI 2021
TabNet: Attentive Interpretable Tabular Learning AAAI 2021
Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms AAAI 2021