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Xiaoting Shao
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2020 – today
- 2022
- [b1]Xiaoting Shao:
Explaining and Interactively Debugging Deep Models. Technical University of Darmstadt, Germany, 2022 - [j4]Xiaoting Shao
, Alejandro Molina, Antonio Vergari
Conditional sum-product networks: Modular probabilistic circuits via gate functions. Int. J. Approx. Reason. 140: 298-313 (2022) - [i5]Xiaoting Shao, Karl Stelzner, Kristian Kersting:
Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement. CoRR abs/2202.00391 (2022) - [i4]Xiaoting Shao, Kristian Kersting:
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models. CoRR abs/2205.07774 (2022) - 2021
- [c5]Xiaoting Shao, Arseny Skryagin, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting:
Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions. AAAI 2021: 9533-9540 - 2020
- [j3]Xiaoting Shao
Towards Understanding and Arguing with Classifiers: Recent Progress. Datenbank-Spektrum 20(2): 171-180 (2020) - [j2]Patrick Schramowski
Making deep neural networks right for the right scientific reasons by interacting with their explanations. Nat. Mach. Intell. 2(8): 476-486 (2020) - [c4]Tjitze Rienstra, Matthias Thimm, Kristian Kersting, Xiaoting Shao:
Independence and D-separation in Abstract Argumentation. KR 2020: 713-722 - [c3]Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting:
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. PGM 2020: 401-412 - [c2]Xiaoting Shao, Zhongjie Yu, Arseny Skryagin, Tjitze Rienstra, Matthias Thimm, Kristian Kersting:
Modelling Multivariate Ranking Functions with Min-Sum Networks. SUM 2020: 281-288 - [i3]Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting:
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations. CoRR abs/2001.05371 (2020)
2010 – 2019
- 2019
- [j1]Andrea Galassi
Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning. Frontiers Big Data 2: 52 (2019) - [c1]Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. UAI 2019: 334-344 - [i2]Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting:
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. CoRR abs/1905.08550 (2019) - [i1]Andrea Galassi
Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning. CoRR abs/1905.09103 (2019)
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