Unsupervised Program Synthesis: Hierarchy and Perception
Tue 14 Jun 2022 03:30 - 04:15 at Boardroom - Evening
How can we discover interpretable patterns and regularities in datasets? I will discuss a specific approach to this problem, called unsupervised program synthesis, which seeks to synthesize generative programs that reconstruct the input data. This talk will give two applications of this framework:
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Discovering language patterns in linguistics problems. The talk will show that by doing hierarchical Bayesian inference across many linguistics problems for many different languages, the unsupervised learner can better discover regularities in how natural languages build words.
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Applying unsupervised program synthesis to raw perceptual data. The end of the talk will show early work on applying the framework to learning game-rules from pixel input. This second part is work with Richard Evans.
Mon 13 JunDisplayed time zone: Pacific Time (US & Canada) change
15:30 - 17:00 | |||
15:30 45mKeynote | Unsupervised Program Synthesis: Hierarchy and Perception MAPS Kevin Ellis Cornell University | ||
16:15 15mTalk | ExeBench: An ML-scale dataset of executable C functions MAPS Jordi Armengol-Estapé University of Edinburgh, Jackson Woodruff University of Edinburgh, Alexander Brauckmann University of Edinburgh, José Wesley de Souza Magalhães University of Edinburgh, Michael F. P. O'Boyle University of Edinburgh | ||
16:30 15mTalk | Automatically Debugging AutoML Pipelines Using Maro: ML Automated Remediation Oracle MAPS | ||
16:45 15mTalk | A Graph Neural Network-based performance model for Deep Learning Applications MAPS Shikhar Singh University of Texas, James Hegarty Facebook, Hugh Leather University of Edinburgh, UK, Benoit Steiner Facebook | ||
Tue 14 JunDisplayed time zone: Pacific Time (US & Canada) change
03:30 - 05:00 | |||
03:30 45mKeynote | Unsupervised Program Synthesis: Hierarchy and Perception MAPS Kevin Ellis Cornell University | ||
04:15 15mTalk | ExeBench: An ML-scale dataset of executable C functions MAPS Jordi Armengol-Estapé University of Edinburgh, Jackson Woodruff University of Edinburgh, Alexander Brauckmann University of Edinburgh, José Wesley de Souza Magalhães University of Edinburgh, Michael F. P. O'Boyle University of Edinburgh | ||
04:30 15mTalk | Automatically Debugging AutoML Pipelines Using Maro: ML Automated Remediation Oracle MAPS | ||
04:45 15mTalk | A Graph Neural Network-based performance model for Deep Learning Applications MAPS Shikhar Singh University of Texas, James Hegarty Facebook, Hugh Leather University of Edinburgh, UK, Benoit Steiner Facebook | ||