Querying Word Embeddings for Similarity and Relatedness
Fatemeh Torabi Asr, Robert Zinkov, and Michael N. Jones
In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2018
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Automating Expectation Maximixation
Robert Zinkov
In NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
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End-to-end Training of Differentiable Pipelines Across Machine Learning Frameworks
Mitar Milutinovic, Atılım Güneş Baydin, Robert Zinkov, William Harvey, Dawn Song, Frank Wood, and Wade Shen
In NIPS Workshop on Autodiff, 2017
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Principled Inference Networks in Deep Generative Models
Stefan Webb, Adam Golinski, Robert Zinkov, and Frank Wood
In NIPS Workshop on Bayesian Deep Learning, 2017
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Composing inference algorithms as program transformations
Robert Zinkov and Chung-chieh Shan
In Proceedings of Uncertainty in Artificial Intelligence, 2017
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Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Tuan Anh Le, Atılım Güneş Baydin, Robert Zinkov, and Frank Wood
In International Joint Conference on Neural Networks (IJCNN), 2017
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Probabilistic Inference by Program Transformation in Hakaru (System Description)
Praveen Narayanan, Jacques Carette, Wren Romano, Chung-chieh Shan, and Robert Zinkov
In Functional and Logic Programming: 13th International Symposium, FLOPS 2016, 2016
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Building blocks for exact and approximate inference
Jacques Carette, Praveen Narayanan, Wren Romano, Chung-chieh Shan, and Robert Zinkov
In NIPS Workshop on Black Box Learning and Inference, 2015
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Designing a MCMC library for Probabilistic Programming
Robert Zinkov, Praveen Narayanan, and Chung-chieh Shan
In PLDI Workshop on Probabilistic and Approximate Computing, 2014
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Sensitivity analysis for distributed optimization with resource constraints
Emma Bowring, Zhengyu Yin, Rob Zinkov, and Milind Tambe
In 8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2009
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Potential-based Shaping in Model-based Reinforcement Learning
John Asmuth, Michael L. Littman, and Robert Zinkov
In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI), 2008
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