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Country: United States
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Latitude: 37.751
Longitude: -97.822




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Overview Speakers Schedule Organizers Papers NEW: Submit your questions for the panel here DATE AND TIME: 8:30 AM - 6:00 PM, December 8, 2018 LOCATION: Room 517A, Palais des Congrès de Montréal, Montréal, give rise to complex phenomena in the everyday world. Well-known applications include knowledge base completion and social network an*lysis. Although many relational datasets are available, integrating them directly into modern machine learning algorithms and systems that rely on continuous, gradient-based optimization and make strong i.i.d. as*umptions is challenging. Relational representation learning has the potential to overcome these obstacles: it enables the fusion of recent advancements like de*p learning and relational reasoning to learn from high-dimensional data. Success of such methods can facilitate novel applications of relational reasoning in areas such as scene understanding, visual question-answering, understanding chemical and biological processes, program synthesis and an*lysis, decision-making How should we rethink clas*ical representation learning theory for relational representations? Clas*ical approaches based on dimensionality reduction techniques such as isoMap and spectral decompositions still serve as strong baselines and are slowly paving the way for modern methods in relational representation learning based on random walks over graphs, message-pas*ing in neural networks, group-invariant de*p architectures etc. amongst many others. How can systems be designed and potentially deployed for large scale representation learning? What are promising avenues, beyond traditional applications like knowledge base and social network an*lysis, that can benefit from relational representation learning? This workshop aims to bring together researchers from both academia and industry interested in addressing various aspects of Moderator: Paroma Varma Panelists: Aditya Grover, William Hamilton, Jessica Hamrick, Thomas Petar Veličković, Liam Fedus, William Hamilton, Pietro Liò, Yoshua Bengio, Devon Hjelm


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Title: Relational Representation Learning | NeurIPS 2018 Could be improved
Description: NeurIPS 2018 Could be improved
H1: Relational Representation LearningIs it informative enough?
H2: NeurIPS 2018 WorkshopIs it informative enough?

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