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Interpretability

349 directly classified papers

Papers per year

Papers

Modeling Paths for Explainable Knowledge Base Completion ACL 2019
On the Realization of Compositionality in Neural Networks ACL 2019
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models NIPS 2018
Model Agnostic Supervised Local Explanations NIPS 2018
Portable, layer-wise task performance monitoring for NLP models EMNLP 2018
An Analysis of Encoder Representations in Transformer-Based Machine Translation EMNLP 2018
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties ACL 2018
Interpretable Convolutional Neural Networks CVPR 2018
Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks CVPR 2018
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations NIPS 2018
Boolean Decision Rules via Column Generation NIPS 2018
Rule induction for global explanation of trained models EMNLP 2018
A Unified Approach to Interpreting Model Predictions NIPS 2017
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly NIPS 2017
Improving Interpretability of Deep Neural Networks With Semantic Information CVPR 2017
Mining Object Parts From CNNs via Active Question-Answering CVPR 2017
Inverting Visual Representations With Convolutional Networks CVPR 2016
Monotonic Calibrated Interpolated Look-Up Tables JMLR 2016
Fast and Flexible Monotonic Functions with Ensembles of Lattices NIPS 2016
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks NIPS 2016
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions NIPS 2015
Understanding Classifier Errors by Examining Influential Neighbors CVPR 2015
Partition-wise Linear Models NIPS 2014
Interpreting the neural code with Formal Concept Analysis NIPS 2008