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Daniel Tarlow

Learning Generalized Gumbel-max Causal Mechanisms


Nov 11, 2021
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan

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* Accepted to NeurIPS 2021 (Spotlight) 
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Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models


Jul 16, 2021
Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow

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* Accepted at the ICML 2021 Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (poster) 
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Structured Denoising Diffusion Models in Discrete State-Spaces


Jul 13, 2021
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

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* 10 pages plus references and appendices. First two authors contributed equally 
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Learning to Combine Per-Example Solutions for Neural Program Synthesis


Jun 14, 2021
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

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Learning to Extend Program Graphs to Work-in-Progress Code


May 28, 2021
Xuechen Li, Chris J. Maddison, Daniel Tarlow

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Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks


Oct 23, 2020
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

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* Accepted at NeurIPS 2020 
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Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs


Jul 13, 2020
Kian Ahrabian, Daniel Tarlow, Hehuimin Cheng, Jin L. C. Guo

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* 11 pages, 1 figure. 37th International Conference on Machine Learning (ICML 2020) - Workshop on Graph Representation Learning and Beyond 
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Learning Graph Structure With A Finite-State Automaton Layer


Jul 09, 2020
Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow

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* Submitted to NeurIPS 2020 
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Gradient Estimation with Stochastic Softmax Tricks


Jun 15, 2020
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

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On-the-Fly Adaptation of Source Code Models using Meta-Learning


Mar 26, 2020
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

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Learning to Fix Build Errors with Graph2Diff Neural Networks


Nov 04, 2019
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian

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* Submitted for review on Aug 23, 2019 
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Fast Training of Sparse Graph Neural Networks on Dense Hardware


Jun 27, 2019
Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow

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Learning Execution through Neural Code Fusion


Jun 17, 2019
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

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* 14 pages,7 figures 
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Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces


Jun 14, 2019
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

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Neural Networks for Modeling Source Code Edits


Apr 04, 2019
Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow

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* Deanonymized version of ICLR 2019 submission 
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Graph Partition Neural Networks for Semi-Supervised Classification


Mar 16, 2018
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel

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Gated Graph Sequence Neural Networks


Sep 22, 2017
Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel

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* Published as a conference paper in ICLR 2016. Fixed a typo 
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AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks


Jun 22, 2017
Alexander L. Gaunt, Matthew A. Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster

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* 17 pages, 13 figures 
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DeepCoder: Learning to Write Programs


Mar 08, 2017
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow

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* Submitted to ICLR 2017 
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Differentiable Programs with Neural Libraries


Mar 02, 2017
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

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Differentiable Functional Program Interpreters


Mar 02, 2017
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow

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Summary - TerpreT: A Probabilistic Programming Language for Program Induction


Dec 02, 2016
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

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* 7 pages, 2 figures, 4 tables in 1st Workshop on Neural Abstract Machines & Program Induction (NAMPI), @NIPS 2016 
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TerpreT: A Probabilistic Programming Language for Program Induction


Aug 15, 2016
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

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* 50 pages, 20 figures, 4 tables 
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Consensus Message Passing for Layered Graphical Models


Jan 26, 2015
Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn

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* Appearing in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 2015 
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A* Sampling


Jan 26, 2015
Chris J. Maddison, Daniel Tarlow, Tom Minka

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* V2: - reworded the last paragraph of Section 2 to clarify that the argmax is a sample from the normalized measure. - fixed notation in Algorithm 1. - fixed a typo in paragraph 2 of Section 5 
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Candidate Constrained CRFs for Loss-Aware Structured Prediction


Dec 10, 2014
Faruk Ahmed, Daniel Tarlow, Dhruv Batra

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* 20 pages including Supplement 
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Structured Generative Models of Natural Source Code


Jun 20, 2014
Chris J. Maddison, Daniel Tarlow

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Detecting Parameter Symmetries in Probabilistic Models


Dec 19, 2013
Robert Nishihara, Thomas Minka, Daniel Tarlow

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* 24 pages, 8 figures 
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Tighter Linear Program Relaxations for High Order Graphical Models


Sep 26, 2013
Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss

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* Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013) 
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