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Interpretability

7,452 papers

Papers per year

5
14
488
1716
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Papers

Small Data, Big Decisions: Model Selection in the Small-Data Regime ICML 2020
Concise Explanations of Neural Networks using Adversarial Training ICML 2020
Invariant Rationalization ICML 2020
Mapping natural-language problems to formal-language solutions using structured neural representations ICML 2020
Interpreting Robust Optimization via Adversarial Influence Functions ICML 2020
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions ICML 2020
Multidimensional Shape Constraints ICML 2020
Concept Bottleneck Models ICML 2020
Problems with Shapley-value-based explanations as feature importance measures ICML 2020
Robust and Stable Black Box Explanations ICML 2020
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning ICML 2020
Generalized and Scalable Optimal Sparse Decision Trees ICML 2020
How recurrent networks implement contextual processing in sentiment analysis ICML 2020
Confidence-Aware Learning for Deep Neural Networks ICML 2020
Explainable k-Means and k-Medians Clustering ICML 2020
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis ICML 2020
Explaining Groups of Points in Low-Dimensional Representations ICML 2020
DeepCoDA: personalized interpretability for compositional health data ICML 2020
Transparency Promotion with Model-Agnostic Linear Competitors ICML 2020
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge ICML 2020
Reverse-engineering deep ReLU networks ICML 2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations ICML 2020
When Explanations Lie: Why Many Modified BP Attributions Fail ICML 2020
The Shapley Taylor Interaction Index ICML 2020
Uncertainty Estimation Using a Single Deep Deterministic Neural Network ICML 2020