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Interpretability

7318 directly classified papers

Papers per year

Papers

Axiomatic Attribution for Deep Networks ICML 2017
Epistemic-entrenchment Characterization of Parikh’s Axiom IJCAI 2017
Automatic Extraction of News Values from Headline Text EACL 2017
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds IJCAI 2017
Joint Concept Learning and Semantic Parsing from Natural Language Explanations EMNLP 2017
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy IJCAI 2017
Human-Centric Justification of Machine Learning Predictions IJCAI 2017
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain AISTATS 2017
Understanding Black-box Predictions via Influence Functions ICML 2017
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability ICML 2017
A Multidimensional Lexicon for Interpersonal Stancetaking ACL 2017
Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems ACL 2017
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network NIPS 2017
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin NIPS 2017
From Bayesian Sparsity to Gated Recurrent Nets NIPS 2017
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly NIPS 2017
Top-Down Visual Saliency Guided by Captions CVPR 2017
A Deep Network with Visual Text Composition Behavior ACL 2017
Temporal Attention-Gated Model for Robust Sequence Classification CVPR 2017
Making the v in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering CVPR 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations CVPR 2017
From Red Wine to Red Tomato: Composition With Context CVPR 2017
A Regularized Framework for Sparse and Structured Neural Attention NIPS 2017
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts NIPS 2017
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability NIPS 2017