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Interpretability

7318 directly classified papers

Papers per year

Papers

How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods NIPS 2020
Feature Importance Ranking for Deep Learning NIPS 2020
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks NIPS 2020
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models NIPS 2020
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses NIPS 2020
Lipschitz-Certifiable Training with a Tight Outer Bound NIPS 2020
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class NIPS 2020
X-CAL: Explicit Calibration for Survival Analysis NIPS 2020
Emotion Arcs of Student Narratives ACL 2020
Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder ACL 2020
Story-level Text Style Transfer: A Proposal ACL 2020
Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention ACL 2020
“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition ACL 2020
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond NIPS 2020
Compositional Generalization via Neural-Symbolic Stack Machines NIPS 2020
Efficient Exact Verification of Binarized Neural Networks NIPS 2020
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics NIPS 2020
Consistent feature selection for analytic deep neural networks NIPS 2020
Learning Global Transparent Models consistent with Local Contrastive Explanations NIPS 2020
Evaluating Attribution for Graph Neural Networks NIPS 2020
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions NIPS 2020
Detecting Interactions from Neural Networks via Topological Analysis NIPS 2020
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations NIPS 2020
Benchmarking Deep Learning Interpretability in Time Series Predictions NIPS 2020
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images NIPS 2020