model interpretability

Transparent Classification with Multilayer Logical Perceptrons and Random Binarization AAAI 2020 Understanding Neural Abstractive Summarization Models via Uncertainty EMNLP 2020 XplaiNLI: Explainable Natural Language Inference through Visual Analytics COLING 2020 Abduction-Based Explanations for Machine Learning Models AAAI 2019 A Multiscale Visualization of Attention in the Transformer Model ACL 2019 Auditing Deep Learning processes through Kernel-based Explanatory Models EMNLP 2019 Building Human-Machine Trust via Interpretability AAAI 2019 Deep Neural Model Inspection and Comparison via Functional Neuron Pathways ACL 2019 Detecting and Reducing Bias in a High Stakes Domain EMNLP 2019 Evaluating Recurrent Neural Network Explanations ACL 2019 Explicit Bias Discovery in Visual Question Answering Models CVPR 2019 Faithful Multimodal Explanation for Visual Question Answering ACL 2019 Graph Space Embedding IJCAI 2019 Incorporating Priors with Feature Attribution on Text Classification ACL 2019 Interpreting Black Box Predictions using Fisher Kernels AISTATS 2019 Interpreting CNNs via Decision Trees CVPR 2019 Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods AAAI 2019 Interrogating the Explanatory Power of Attention in Neural Machine Translation EMNLP 2019 Is Attention Interpretable? ACL 2019 NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks AAAI 2019 Probing Neural Network Comprehension of Natural Language Arguments ACL 2019 QADiver: Interactive Framework for Diagnosing QA Models AAAI 2019 Revealing the Dark Secrets of BERT EMNLP 2019 Saliency Learning: Teaching the Model Where to Pay Attention NAACL 2019 Towards Debugging Deep Neural Networks by Generating Speech Utterances Interspeech 2019