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Interpretability

349 directly classified papers

Papers per year

Papers

Explicit Bias Discovery in Visual Question Answering Models CVPR 2019
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression CVPR 2019
Visualizing and Measuring the Geometry of BERT NIPS 2019
Robust Attribution Regularization NIPS 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty NIPS 2019
Demystifying Black-box Models with Symbolic Metamodels NIPS 2019
Grid Saliency for Context Explanations of Semantic Segmentation NIPS 2019
Approximate Feature Collisions in Neural Nets NIPS 2019
Accurate Layerwise Interpretable Competence Estimation NIPS 2019
On the Accuracy of Influence Functions for Measuring Group Effects NIPS 2019
A Benchmark for Interpretability Methods in Deep Neural Networks NIPS 2019
Evaluating Recurrent Neural Network Explanations ACL 2019
Incorporating Priors with Feature Attribution on Text Classification ACL 2019
Towards Better Interpretability in Deep Q-Networks AAAI 2019
FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions AAAI 2019
Abduction-Based Explanations for Machine Learning Models AAAI 2019
Axiomatic Characterization of Data-Driven Influence Measures for Classification AAAI 2019
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach NAACL 2019
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models EMNLP 2019
Human-grounded Evaluations of Explanation Methods for Text Classification EMNLP 2019
Analytical Methods for Interpretable Ultradense Word Embeddings EMNLP 2019
Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods AAAI 2019
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals AAAI 2019
NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks AAAI 2019
Verification of RNN-Based Neural Agent-Environment Systems AAAI 2019