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Posters
Colin Graber · Yuan-Ting Hu · Tiantian Fang · Jessica Hamrick · Giorgio Giannone · John Co-Reyes · Boyang Deng · Eric Crawford · Andrea Dittadi · Peter Karkus · Matthew Dirks · Rakshit Trivedi · Sunny Raj · Javier Felip Leon · Harris Chan · Jan Chorowski · Jeff Orchard · Aleksandar Stanić · Adam Kortylewski · Ben Zinberg · Chenghui Zhou · Wei Sun · Vikash Mansinghka · Chun-Liang Li · Marco Cusumano-Towner

Author Information

Colin Graber (University of Illinois at Urbana-Champaign)
Yuan-Ting Hu (University of Illinois Urbana-Champaign)
Tiantian Fang (University of Illinois Urbana-Champaign)
Jessica Hamrick (DeepMind)
Giorgio Giannone (NNAISENSE)

Science is built up with data, as a house is with stones. But a collection of data is no more a science than a heap of stones is a house. (J.H. Poincaré)

John Co-Reyes (UC Berkeley)

Interested in solving intelligence. Currently working on hierarchical reinforcement learning and learning a physical intuition of the world.

Boyang Deng (Google)
Eric Crawford (McGill University)
Andrea Dittadi (Technical University of Denmark)
Peter Karkus (NUS)
Matthew Dirks (University of British Columbia)
Rakshit Trivedi (GEORGIA INSTITUTE OF TECHNOLOGY)
Sunny Raj (University of Central Florida)
Javier Felip Leon (Intel Corporation)
Harris Chan (University of Toronto, Vector Institute)
Jan Chorowski (University of Wroclaw)
Jeff Orchard (University of Waterloo)

Jeff Orchard received degrees in applied mathematics from the University of Waterloo (BMath) and the University of British Columbia (MSc), and received his PhD in Computing Science from Simon Fraser University in 2003. Since then, he has been a faculty member in the Cheriton School of Computer Science at the University of Waterloo. His main research focus is on computational neuroscience and artificial intelligence, using mathematical models and computer simulations of neural networks to understand how the brain works. He has also published research papers in image processing, medical imaging, and graphics.

Aleksandar Stanić (Swiss AI lab IDSIA)
Adam Kortylewski (Johns Hopkins University)
Ben Zinberg (MIT)
Chenghui Zhou (Carnegie Mellon University)
Wei Sun (University of Waterloo)
Vikash Mansinghka (Massachusetts Institute of Technology)

Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He served on DARPA’s Information Science and Technology advisory board from 2010-2012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation. He was an advisor to Google DeepMind and has co-founded two AI-related startups, one acquired and one currently operational.

Chun-Liang Li (Carnegie Mellon University)
Marco Cusumano-Towner (Massachusetts Institute of Technology)

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  • 2019 : Poster Session »
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  • 2019 : Contributed Talk - Chirality Nets: Exploiting Structure in Human Pose Regression »
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  • 2019 Poster: Graph Structured Prediction Energy Networks »
    Colin Graber · Alex Schwing
  • 2019 Poster: Attribution-Based Confidence Metric For Deep Neural Networks »
    Susmit Jha · Sunny Raj · Steven Fernandes · Sumit K Jha · Somesh Jha · Brian Jalaian · Gunjan Verma · Ananthram Swami
  • 2019 Poster: Co-Generation with GANs using AIS based HMC »
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  • 2018 : Panel »
    Paroma Varma · Aditya Grover · Will Hamilton · Jessica Hamrick · Thomas Kipf · Marinka Zitnik
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  • 2018 : Poster Sessions and Lunch (Provided) »
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  • 2017 Poster: MMD GAN: Towards Deeper Understanding of Moment Matching Network »
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  • 2017 Poster: QMDP-Net: Deep Learning for Planning under Partial Observability »
    Peter Karkus · David Hsu · Wee Sun Lee
  • 2017 Tutorial: Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning »
    Josh Tenenbaum · Vikash Mansinghka
  • 2016 Poster: A Probabilistic Programming Approach To Probabilistic Data Analysis »
    Feras Saad · Vikash Mansinghka
  • 2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
    Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden
  • 2013 Poster: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
    Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum
  • 2013 Oral: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
    Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum
  • 2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
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  • 2009 Demonstration: Monte: An Interactive Ssytem for Massively Parallel Probabilistic Programming »
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  • 2009 Demonstration: The IID: A Natively Probabilistic Reconfigurable Computer »
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    Daniel Roy · John Winn · David A McAllester · Vikash Mansinghka · Josh Tenenbaum
  • 2006 Poster: Learning annotated hierarchies from relational data »
    Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum
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    Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum
  • 2006 Demonstration: Blaise: A System for Interactive Development of High Performance Inference Algorithms »
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