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1802.06259
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Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
17 February 2018
Lingyang Chu
X. Hu
Juhua Hu
Lanjun Wang
Jian Pei
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Papers citing
"Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution"
46 / 46 papers shown
Title
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A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation
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Deep Contract Design via Discontinuous Networks
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Paul Dutting
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Deep ReLU Networks Have Surprisingly Simple Polytopes
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When Deep Learning Meets Polyhedral Theory: A Survey
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Calvin Tsay
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29 Apr 2023
The Power of Typed Affine Decision Structures: A Case Study
International Journal on Software Tools for Technology Transfer (STTT) (STTT), 2023
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Maximilian Schlüter
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Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness
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24 Apr 2023
Model Doctor for Diagnosing and Treating Segmentation Error
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Lin Chen
Kaiwen Hu
Lechao Cheng
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Min-Gyoo Song
174
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Towards Rigorous Understanding of Neural Networks via Semantics-preserving Transformations
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237
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Non-Linear Coordination Graphs
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A Survey of Neural Trees
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On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks
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Ehtsham Elahi
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160
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Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Proceedings of the IEEE (Proc. IEEE), 2022
He Zhang
Bang Wu
Lizhen Qu
Shirui Pan
Hanghang Tong
Jian Pei
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A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
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Daniel Lundstrom
Tianjian Huang
Meisam Razaviyayn
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From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
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Meike Nauta
Jan Trienes
Shreyasi Pathak
Elisa Nguyen
Michelle Peters
Yasmin Schmitt
Jorg Schlotterer
M. V. Keulen
C. Seifert
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20 Jan 2022
Traversing the Local Polytopes of ReLU Neural Networks: A Unified Approach for Network Verification
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J. Vaughan
Jie Chen
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Agus Sudjianto
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135
15
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17 Nov 2021
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation
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Jing-Xiao Liao
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Yixin Chen
Bingguo Liu
Dong Ye
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Scalable Rule-Based Representation Learning for Interpretable Classification
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139
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Deep Active Learning for Text Classification with Diverse Interpretations
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Yanqiao Zhu
Zhaocheng Liu
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Finding Representative Interpretations on Convolutional Neural Networks
IEEE International Conference on Computer Vision (ICCV), 2021
P. C. Lam
Lingyang Chu
Maxim Torgonskiy
Jian Pei
Yong Zhang
Lanjun Wang
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159
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13 Aug 2021
Robust Counterfactual Explanations on Graph Neural Networks
Neural Information Processing Systems (NeurIPS), 2021
Mohit Bajaj
Lingyang Chu
Zihui Xue
Jian Pei
Lanjun Wang
P. C. Lam
Yong Zhang
OOD
378
113
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08 Jul 2021
Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Jiabao Lei
Kui Jia
Yi-An Ma
141
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How to Explain Neural Networks: an Approximation Perspective
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Bingguo Liu
Fengdong Chen
Dong Ye
Guodong Liu
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143
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DNN2LR: Automatic Feature Crossing for Credit Scoring
Qiang Liu
Zhaocheng Liu
Haoli Zhang
Yuntian Chen
Jun Zhu
121
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The Self-Simplifying Machine: Exploiting the Structure of Piecewise Linear Neural Networks to Create Interpretable Models
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Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
Agus Sudjianto
William Knauth
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DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
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Qiang Liu
Haoli Zhang
Yuntian Chen
170
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Deep Active Learning by Model Interpretability
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Zhaocheng Liu
Xiaofang Zhu
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206
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Explainable Artificial Intelligence: a Systematic Review
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Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
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Jiabao Lei
Kui Jia
116
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Quasi-Equivalence of Width and Depth of Neural Networks
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Rongjie Lai
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482
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Jinjun Xiong
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Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
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Wei Zhang
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128
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GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction
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Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution
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Zicun Cong
Lingyang Chu
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X. Hu
Jian Pei
773
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Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
International Conference on Machine Learning (ICML), 2019
Tian Guo
Tao Lin
Nino Antulov-Fantulin
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On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
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Ninghao Liu
Fan Yang
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Multi-Granularity Reasoning for Social Relation Recognition from Images
Meng Zhang
Xinchen Liu
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Anfu Zhou
Huadong Ma
Tao Mei
159
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Soft Autoencoder and Its Wavelet Adaptation Interpretation
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Mengzhou Li
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195
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A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
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Emese Thamo
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A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
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1