Functional Transparency for Structured Data: a Game-Theoretic Approach

26 February 2019

David Alvarez-Melis

Papers citing "Functional Transparency for Structured Data: a Game-Theoretic Approach"

33 / 33 papers shown

Title
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models Jayneel Parekh Quentin Bouniot Pavlo Mozharovskyi A. Newson Florence dÁlché-Buc SSL 135 1 0 01 Jul 2024
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 527 10,591 0 17 Feb 2020
Towards Robust, Locally Linear Deep Networks Guang-He Lee David Alvarez-Melis Tommi Jaakkola ODL 119 48 0 07 Jul 2019
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh Joon Sik Kim Ian En-Hsu Yen Pradeep Ravikumar TDI 92 254 0 23 Nov 2018
Game-Theoretic Interpretability for Temporal Modeling Guang-He Lee David Alvarez-Melis Tommi Jaakkola AI4TS 30 6 0 30 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 89 528 0 21 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 130 948 0 20 Jun 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 359 1,371 0 12 Feb 2018
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 130 283 0 16 Nov 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 150 871 0 29 Oct 2017
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform Kyle E. Helfrich Devin Willmott Qiang Ye 95 131 0 29 Jul 2017
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 359 204 0 06 Jul 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 803 132,454 0 12 Jun 2017
The Cramer Distance as a Solution to Biased Wasserstein Gradients Marc G. Bellemare Ivo Danihelka Will Dabney S. Mohamed Balaji Lakshminarayanan Stephan Hoyer Rémi Munos GAN 87 344 0 30 May 2017
Contextual Explanation Networks Maruan Al-Shedivat Kumar Avinava Dubey Eric Xing CML 76 83 0 29 May 2017
Deriving Neural Architectures from Sequence and Graph Kernels Tao Lei Wengong Jin Regina Barzilay Tommi Jaakkola GNN 88 137 0 25 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 150 808 0 28 Apr 2017
Improved Training of Wasserstein GANs Ishaan Gulrajani Faruk Ahmed Martín Arjovsky Vincent Dumoulin Aaron Courville GAN 227 9,564 0 31 Mar 2017
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 343 1,839 0 02 Mar 2017
Wasserstein GAN Martín Arjovsky Soumith Chintala Léon Bottou GAN 179 4,829 0 26 Jan 2017
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach L. Arras F. Horn G. Montavon K. Müller Wojciech Samek 77 288 0 23 Dec 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 335 20,110 0 07 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 807 36,892 0 25 Aug 2016
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 129 812 0 13 Jun 2016
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 433 18,361 0 27 May 2016
Discriminative Embeddings of Latent Variable Models for Structured Data H. Dai Bo Dai Le Song BDL 122 697 0 17 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,510 0 10 Dec 2015
Domain-Adversarial Training of Neural Networks Yaroslav Ganin E. Ustinova Hana Ajakan Pascal Germain Hugo Larochelle François Laviolette M. Marchand Victor Lempitsky GAN OOD 390 9,524 0 28 May 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,364 0 22 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Understanding Deep Image Representations by Inverting Them Aravindh Mahendran Andrea Vedaldi FAtt 131 1,967 0 26 Nov 2014
The Sample Complexity of Learning Linear Predictors with the Squared Loss Ohad Shamir 79 41 0 19 Jun 2014