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Controlling Neural Networks with Rule Representations

14 June 2021

Tomas Pfister

Papers citing "Controlling Neural Networks with Rule Representations"

23 / 23 papers shown

Title
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 117 687 0 06 Nov 2020
MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention Carson Eisenach Yagna Patel Dhruv Madeka AI4TS 59 37 0 30 Sep 2020
Very Deep Transformers for Neural Machine Translation Xiaodong Liu Kevin Duh Liyuan Liu Jianfeng Gao 60 104 0 18 Aug 2020
Lagrangian Duality for Constrained Deep Learning Ferdinando Fioretto Pascal Van Hentenryck Terrence W.K. Mak Cuong Tran Federico Baldo M. Lombardi PINN 47 83 0 26 Jan 2020
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting Bryan Lim Sercan O. Arik Nicolas Loeff Tomas Pfister AI4TS 118 1,463 0 19 Dec 2019
Newton vs the machine: solving the chaotic three-body problem using deep neural networks Philip G. Breen Christopher N. Foley Tjarda Boekholt Simon Portegies Zwart AI4CE 51 73 0 16 Oct 2019
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data Sergei Popov S. Morozov Artem Babenko LMTD 137 314 0 13 Sep 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 67 392 0 06 Sep 2019
TabNet: Attentive Interpretable Tabular Learning Sercan O. Arik Tomas Pfister LMTD 188 1,353 0 20 Aug 2019
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning M. Lutter Christian Ritter Jan Peters PINN AI4CE 60 379 0 10 Jul 2019
Fixing the train-test resolution discrepancy Hugo Touvron Andrea Vedaldi Matthijs Douze Hervé Jégou 119 423 0 14 Jun 2019
Hamiltonian Neural Networks S. Greydanus Misko Dzamba J. Yosinski PINN AI4CE 118 894 0 04 Jun 2019
Boolean Decision Rules via Column Generation S. Dash Oktay Gunluk Dennis L. Wei 67 174 0 24 May 2018
Mitigating Unwanted Biases with Adversarial Learning B. Zhang Blake Lemoine Margaret Mitchell FaML 197 1,389 0 22 Jan 2018
Adversarial Examples: Attacks and Defenses for Deep Learning Xiaoyong Yuan Pan He Qile Zhu Xiaolin Li SILM AAML 94 1,622 0 19 Dec 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 280 9,764 0 25 Oct 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 707 131,652 0 12 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 402 3,798 0 28 Feb 2017
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 543 5,897 0 08 Jul 2016
Harnessing Deep Neural Networks with Logic Rules Zhiting Hu Xuezhe Ma Zhengzhong Liu Eduard H. Hovy Eric Xing AI4CE NAI 56 614 0 21 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 883 27,373 0 02 Dec 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,066 0 20 Dec 2014