Interpretability

Turning 30: New Ideas in Inductive Logic Programming IJCAI 2020 TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP UAI 2020 Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End CVPR 2020 Uncertainty-Aware Deep Classifiers Using Generative Models AAAI 2020 Uncertainty Estimation Using a Single Deep Deterministic Neural Network ICML 2020 Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks COLING 2020 Uncertainty Quantification for Sparse Deep Learning AISTATS 2020 Understanding Alzheimer disease’s structural connectivity through explainable AI MIDL 2020 Understanding Attention for Text Classification ACL 2020 Understanding Global Feature Contributions With Additive Importance Measures NeurIPS 2020 Understanding Neural Abstractive Summarization Models via Uncertainty EMNLP 2020 Understanding Pre-trained BERT for Aspect-based Sentiment Analysis COLING 2020 Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models AISTATS 2020 Understanding the Limitations of Conditional Generative Models ICLR 2020 Universal Approximation with Certified Networks ICLR 2020 Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains EMNLP 2020 Using deep networks for scientific discovery in physiological signals MLHC 2020 Using Type Information to Improve Entity Coreference Resolution EMNLP 2020 Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints IJCAI 2020 Verifying Individual Fairness in Machine Learning Models UAI 2020 Visual Agreement Regularized Training for Multi-Modal Machine Translation AAAI 2020 Visual Chirality CVPR 2020 Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain AAAI 2020 Weakly- and Semi-supervised Evidence Extraction EMNLP 2020 Weakly-Supervised Action Localization by Generative Attention Modeling CVPR 2020