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While early work on knowledge representation and inference was primarily symbolic, the corresponding approaches subsequently fell out of favor, and were largely supplanted by connectionist methods. In this workshop, we will work to close the gap between the two paradigms, and aim to formulate a new unified approach that is inspired by our current understanding of human cognitive processing. This is important to help improve our understanding of Neural Information Processing and build better Machine Learning systems, including the integration of learning and reasoning in dynamic knowledge-bases, and reuse of knowledge learned in one application domain in analogous domains.
The workshop brings together established leaders and promising young scientists in the fields of neural computation, logic and artificial intelligence, knowledge representation, natural language understanding, machine learning, cognitive science and computational neuroscience. Invited lectures by senior researchers will be complemented with presentations based on contributed papers reporting recent work (following an open call for papers) and a poster session, giving ample opportunity for participants to interact and discuss the complementary perspectives and emerging approaches.
The workshop targets a single broad theme of general interest to the vast majority of the NIPS community, namely translations between connectionist models and symbolic knowledge representation and reasoning for the purpose of achieving an effective integration of neural learning and cognitive reasoning, called neural-symbolic computing. The study of neural-symbolic computing is now an established topic of wider interest to NIPS with topics that are relevant to almost everyone studying neural information processing. In the 2016 edition of the workshop, special emphasis will be put on language-related aspects and applications of neural-symbolic integration and relevant cognitive computation paradigms.
Keywords: neural-symbolic computing; language processing and reasoning; cognitive agents; multimodal learning; deep networks; knowledge extraction; symbol manipulation; variable binding; memory-based networks; dynamic knowledge-bases.
Thu 11:45 p.m. - 12:00 a.m.
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Welcome/Opening
(Introduction)
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Tarek R. Besold, Antoine Bordes, Greg Wayne, Artur Garcez |
Fri 12:00 a.m. - 12:30 a.m.
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"Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs" (Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Rawat Richa)
(Talk)
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Fri 12:30 a.m. - 1:00 a.m.
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Invited talk Barbara Hammer (Bielefeld University, Germany)
(Talk)
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Barbara Hammer |
Fri 1:00 a.m. - 1:30 a.m.
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Invited talk Risto Miikkulainen (University of Texas at Austin & Sentient Technologies Inc., USA)
(Talk)
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Risto Miikkulainen |
Fri 2:00 a.m. - 2:30 a.m.
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Invited talk Kristina Toutanova (Microsoft Research Redmond, USA)
(Talk)
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Kristina N Toutanova |
Fri 2:30 a.m. - 2:50 a.m.
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Poster Pitches
(Spotlight)
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1) "Analogy-based Reasoning With Memory Networks for Future Prediction" (Daniel Andrade, Bing Bai, Ramkumar Rajendran, Yotaro Watanabe) 2) "Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation" (Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron Courville) 3) "Crossmodal language grounding, learning, and teaching" (Stefan Heinrich, Cornelius Weber, Stefan Wermter, Ruobing Xie, Yankai Lin, Zhiyuan Liu) 4) "Diagnostic classifiers: revealing how neural networks process hierarchical structure" (Sara Veldhoen, Dieuwke Hupkes, Willem Zuidema) 5) "Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs" (Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Rawat Richa) "6) Top-Down and Bottom-Up Interactions between Low-Level Reactive Control and Symbolic Rule Learning in Embodied Agents" (Clement Moulin-Frier, Xerxes Arsiwalla, Jordi-Ysard Puigbo, Marti Sanchez-Fibla, Armin Duff, Paul Verschure) 7) "Accuracy and Interpretability Trade-offs in Machine Learning Applied to Safer Gambling" (Sanjoy Sankar, Tillman Weyde, Artur D'Avila Garcez, Gregory Slabaugh, Simo Dragicevic, Chris Percy) 8) "A Simple but Tough-to-Beat Baseline for Sentence Embeddings" (Sanjeev Arora, Yingyu Liang, Tengyu Ma) 9) "MS MARCO: A Human-Generated MAchine Reading COmprehension Dataset" (Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, Li Deng) |
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Fri 2:50 a.m. - 3:45 a.m.
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Poster Presentations
(Poster Session)
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1) "Analogy-based Reasoning With Memory Networks for Future Prediction" (Daniel Andrade, Bing Bai, Ramkumar Rajendran, Yotaro Watanabe) 2) "Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation" (Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron Courville) 3) "Crossmodal language grounding, learning, and teaching" (Stefan Heinrich, Cornelius Weber, Stefan Wermter, Ruobing Xie, Yankai Lin, Zhiyuan Liu) 4) "Diagnostic classifiers: revealing how neural networks process hierarchical structure" (Sara Veldhoen, Dieuwke Hupkes, Willem Zuidema) 5) "Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs" (Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Rawat Richa) "6) Top-Down and Bottom-Up Interactions between Low-Level Reactive Control and Symbolic Rule Learning in Embodied Agents" (Clement Moulin-Frier, Xerxes Arsiwalla, Jordi-Ysard Puigbo, Marti Sanchez-Fibla, Armin Duff, Paul Verschure) 7) "Accuracy and Interpretability Trade-offs in Machine Learning Applied to Safer Gambling" (Sanjoy Sankar, Tillman Weyde, Artur D'Avila Garcez, Gregory Slabaugh, Simo Dragicevic, Chris Percy) 8) "A Simple but Tough-to-Beat Baseline for Sentence Embeddings" (Sanjeev Arora, Yingyu Liang, Tengyu Ma) 9) "MS MARCO: A Human-Generated MAchine Reading COmprehension Dataset" (Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, Li Deng) |
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Fri 3:45 a.m. - 5:00 a.m.
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Lunch break
(Break)
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Fri 5:00 a.m. - 5:20 a.m.
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"Variable binding through assemblies in spiking neural networks" (Robert Legenstein, Christos Papadimitriou, Santosh Vempala, Wolfgang Maass)
(Talk)
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Fri 5:20 a.m. - 5:40 a.m.
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"Pre-Wiring and Pre-Training: What does a neural network need to learn truly general identity rules?" (Raquel Alhama, Willem Zuidema)
(Talk)
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Fri 5:40 a.m. - 6:00 a.m.
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"ReasoNet: Learning to Stop Reading in Machine Comprehension" (Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen)
(Talk)
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Fri 6:00 a.m. - 6:30 a.m.
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Coffee break
(Break)
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Fri 6:30 a.m. - 7:00 a.m.
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Invited talk Dan Roth (University of Illinois at Urbana-Chambaign, USA)
(Talk)
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Dan Roth |
Fri 7:00 a.m. - 8:25 a.m.
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Panel on "Explainable AI" (Yoshua Bengio, Alessio Lomuscio, Gary Marcus, Stephen Muggleton, Michael Witbrock)
(Panel Discussion)
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Yoshua Bengio, Alessio Lomuscio, Gary Marcus, Stephen H Muggleton, Michael Witbrock |
Fri 8:25 a.m. - 8:30 a.m.
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Summary/Goodbye
(Conclusion)
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Tarek R. Besold, Artur Garcez, Antoine Bordes, Greg Wayne |
Author Information
Tarek R. Besold (University of Bremen)
Antoine Bordes (Facebook AI Research)
Greg Wayne (Google DeepMind)
Artur Garcez (City, University of London)
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