Friday, December 13, 2019

Location: West 109 + 110, Area West Level 1

8:00 Opening Remarks
8:05 Invited Talk William W. Cohen, Google AI
Neuro-Symbolic Knowledge Representation
8:35 Contributed Talk Li Li, Minjie Fan, Rishabh Singh and Patrick Riley‎
Neural-Guided Symbolic Regression with Asymptotic Constraints (PDF)
8:50 Contributed Talk Zsolt Zombori, Adrián Csiszárik, Henryk Michalewski, Cezary Kaliszyk and Josef Urban
Towards Finding Longer Proofs (PDF)
9:05 Contributed Talk Giuseppe Marra and Ondřej Kuželka
Neural Markov Logic Networks (PDF)
9:20 Spotlights Poster Spotlights A
9:45 Coffee + Poster Session
10:30 Invited Talk Xin Luna Dong, Amazon
Self-driving Product Understanding for Thousands of Categories
11:00 Contributed Talk Simon Odense and Artur Garcez
Layerwise Knowledge Extraction from Deep Convolutional Networks (PDF)
11:15 Contributed Talk Phung Lai, Hai Phan, David Newman, Han Hu, Anuja Badeti and Dejing Dou
Ontology-based Interpretable Machine Learning with Learnable Anchors (PDF)
11:30 Contributed Talk T. S. Jayram, Tomasz Kornuta, Vincent Albouy, Emre Sevgen and Ahmet Ozcan
Learning Multi-Step Spatio-Temporal Reasoning with Selective Attention Memory Network (PDF)
11:45 Contributed Talk Dmitry Kazhdan, Zohreh Shams and Pietro Lio'
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library (PDF)
12:00 Invited Talk Vivek Srikumar, University of Utah
Training Neural Networks With a Little Help from Knowledge
12:30 Lunch
2:00 Invited Talk Francesca Rossi, IBM T.J. Watson Research Center
Reasoning and Learning Fast and Slow in AI
2:30 Contributed Talk Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Kenneth Forbus and Jianfeng Gao
Best Paper: TP-N2F: Tensor Product Representation for Natural To Formal Language Generation (PDF)
2:45 Contributed Talk Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Wang
TabFact: A Large-scale Dataset for Table-based Fact Verification (PDF)
3:00 Contributed Talk Leonard Adolphs and Thomas Hofmann
Best Paper: LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games (PDF)
3:15 Spotlights Poster Spotlights B
3:30 Coffee + Poster Session
4:15 Invited Talk Yejin Choi, University of Washington/AI2
Cracking Commonsense AI with Knowledge Modeling and Generative Reasoning
4:45 Invited Talk Guy Van den Broeck, UC Los Angeles
Circuit Languages at the Confluence of Learning and Reasoning
5:15 Discussion Panel
5:55 Closing Remarks

Spotlights A

  1. Jiman Kim, Dongha Bahn and Chanjong Park. CEN: Classifier Ensemble Networks based on Joint Optimization of Hyperplanes (PDF)
  2. Xiaoran Xu, Wei Feng, Zhiqing Sun and Zhi-Hong Deng. Neural Consciousness Flow (PDF)
  3. Shih-Chieh Su. Channel Decomposition into Painting Actions (PDF)
  4. Daniel Cunnington, Alessandra Russo, Elisa Bertino and Seraphin Calo. Towards a Coalition Focused Neural-Symbolic Generative Policy Model (PDF)
  5. Wonseok Hwang, Jinyeong Yim, Seunghyun Park and Minjoon Seo. A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization (PDF)
  6. Sarthak Dash, Michael Glass, Alfio Gliozzo and Mustafa Canim. Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation (PDF)
  7. Alberto Camacho and Sheila A. McIlraith. Towards Neural-Guided Program Synthesis for Linear Temporal Logic Specifications (PDF)
  8. Sainyam Galhotra, Udayan Khurana, Oktie Hassanzadeh, Kavitha Srinivas and Horst Samulowitz. KAFE: Automated Feature Enhancement for Predictive Modeling using External Knowledge (PDF)
  9. Shiyang Li, Jianshu Chen and Dian Yu. Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach (PDF)
  10. Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi and Le Song. Can Graph Neural Networks Help Logic Reasoning? (PDF)
  11. Habibeh Naderi Khorshidi, Behrouz Haji Soleimani, Stan Matwin, Sheri Rempel and Rudolf Uher. Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples (PDF)
  12. Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari and Guy Van den Broeck. Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message-Passing (PDF)
  13. Pasha Khosravi, Yoojung Choi, Yitao Liang, Antonio Vergari and Guy Van den Broeck. On Tractable Computation of Expected Prediction (PDF)
  14. Pedro Colon-Hernandez, Henry Lieberman and Catherine Havasi. Does a dog desire cake? - Expanding Knowledge Base Assertions Through Deep Relationship Discovery (PDF)
  15. Vanda Balogh, Gábor Berend, Dimitrios I. Diochnos and György Turán. Understanding the semantic content of sparse word embeddings using a commonsense knowledge base (PDF)
  16. Tehseen Zia, Usman Zahid and David Windridge. A Generative Adversarial Strategy for Modeling Relation Paths in Knowledge Base Representation Learning (PDF)
  17. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester and Luc De Raedt. DeepProbLog: Integrating Logic and Learning through Algebraic Model Counting (PDF)
  18. Ionela Mocanu, Vaishak Belle and Brendan Juba. PAC + SMT (PDF)
  19. Anton Fuxjaeger and Vaishak Belle. Logical Interpretations of Autoencoders (PDF)
  20. Naveen Sundar Govindarajulu and Colin White. Differentiable Functions for Combining First-order Constraints with Deep Learning via Weighted Proof Tracing (PDF)
  21. Joseph Bockhorst, Devin Conathan and Glenn Fung. Knowledge Graph-Driven Conversational Agents (PDF)
  22. Rosario Uceda-Sosa, Nandana Mihindukulasooriya and Atul Kumar. Domain-agnostic construction of domain-specific ontologies (PDF)

Spotlights B

  1. Alberto Camacho and Sheila A. McIlraith. Learning Interpretable Models Expressed in Linear Temporal Logic (PDF)
  2. Simo Dragicevic, Artur Garcez and Chris Percy. Understanding the Risk Profile of Gambling Behaviour through Machine Learning Predictive Modelling and Explanation (PDF)
  3. Lewis Hammond and Vaishak Belle. Tractable Probabilistic Models for Moral Responsibility (PDF)
  4. Giannis Papantonis and Vaishak Belle. Interventions and Counterfactuals in Tractable Probabilistic Models (PDF)
  5. Beliz Gunel, Chenguang Zhu, Michael Zeng and Xuedong Huang. Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization (PDF)
  6. Alberto Camacho, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano and Sheila McIlraith. LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning (PDF)
  7. Radha Manisha Kopparti and Tillman Weyde. Weight Priors for Learning Identity Relations (PDF)
  8. Mohamed Ghalwash, Zijun Yao, Prithwish Chakrabotry, James Codella and Daby Sow. Phenotypical Ontology Driven Framework for Multi-Task Learning (PDF)
  9. Siddhant Arora and Srikanta Bedathur. On Embeddings in Relational Databases (PDF)
  10. León Illanes, Xi Yan, Rodrigo Toro Icarte and Sheila McIlraith. Leveraging Symbolic Planning Models in Hierarchical Reinforcement Learning (PDF)
  11. Susan Zhang, Jonathan Raiman and Filip Wolski. Knowledge Representation and Long Term Planning in OpenAI Five (PDF)
  12. Kaylin Hagopian, Qing Wang, Tengfei Ma, Yupeng Gao and Lingfei Wu. Learning Logical Representations from Natural Languages with Weak Supervision and Back Translation (PDF)
  13. Alexander Lew, Monica Agrawal and Vikash Mansinghka. PClean: Probabilistic Scripts for Automating Common-Sense Data Cleaning
  14. So Yeon Min, Preethi Raghavan and Peter Szolovits. TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces (PDF)