Danny Tarlow

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Core Industry Member
Danny Tarlow
Adjunct Professor, Assistant Professor, McGill University, Google
Danny Tarlow
Learning to Program
Probabilistic Models
http://daniel.tarlow.org/

Danny Tarlow is a research scientist at Google Brain. His main interest is in automatic learning methods for understanding and generating programs. He is also an adjunct professor at the School of Computer Science at McGill University. He holds a PhD from the Automatic Learning Group at the University of Toronto and spent four years as a post-doctoral fellow and then as a researcher at Microsoft Research in Cambridge before moving to Montreal.

Publications

2020-11

OPTIMIZING SPARSE GRAPH NEURAL NETWORKS FOR DENSE HARDWARE
Tarlow Daniel S, Balog Matej, Van Merrienboer Bart, Li Yujia and Moitra Subhodeep
(venue unknown)
(2020-11-26)
lens.org

2020-10

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.
David Bieber, Charles Sutton, Hugo Larochelle and Daniel Tarlow
arXiv: Learning
(2020-10-26)
arxiv.orgPDF

2020-07

Learning Graph Structure With A Finite-State Automaton Layer.
Daniel D. Johnson, Hugo Larochelle and Daniel Tarlow
arXiv: Learning
(2020-07-09)
dblp.uni-trier.dePDF
Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs.
Kian Ahrabian, Daniel Tarlow, Hehuimin Cheng and Jin L. C. Guo
arXiv preprint arXiv:2007.01231
(2020-07-02)
dblp.uni-trier.dePDF

2020-06

Learning to Fix Build Errors with Graph2Diff Neural Networks
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton and Edward Aftandilian
ICSE 2020
(2020-06-27)
dblp.uni-trier.dePDF
Gradient Estimation with Stochastic Softmax Tricks
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause and Chris J. Maddison
NEURIPS 2020
(2020-06-15)
papers.nips.cc
Gradient Estimation with Stochastic Softmax Tricks
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause and Chris J. Maddison
arXiv preprint arXiv:2006.08063
(2020-06-15)
dblp.uni-trier.dePDF

2020-04

LEARNING EXECUTION THROUGH NEURAL CODE FUSION
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan and Milad Hashemi
ICLR 2020
(2020-04-30)
ui.adsabs.harvard.eduPDF[Also on arXiv preprint arXiv:1906.07181 (2019-06-17)]

2020-03

On-the-Fly Adaptation of Source Code Models using Meta-Learning
Disha Shrivastava, Hugo Larochelle and Daniel Tarlow
arXiv preprint arXiv:2003.11768
(2020-03-26)
dblp.uni-trier.dePDF

2020-01

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan and Daniel Tarlow
NEURIPS 2020
(2020-01-01)
papers.nips.cc
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber, Charles Sutton, Hugo Larochelle and Daniel Tarlow
NEURIPS 2020
(2020-01-01)
papers.nips.cc
Learning Graph Structure With A Finite-State Automaton Layer
Daniel D. Johnson, Hugo Larochelle and Daniel Tarlow
NEURIPS 2020
(2020-01-01)
papers.nips.cc

2019-11

Learning to Fix Build Errors with Graph2Diff Neural Networks.
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton and Edward Aftandilian
arXiv preprint arXiv:1911.01205
(2019-11-04)
dblp.uni-trier.dePDF

2019-09

Fast Training of Sparse Graph Neural Networks on Dense Hardware
Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li and Daniel Tarlow
arXiv preprint arXiv:1906.11786
(2019-09-25)
dblp.uni-trier.dePDF

2019-06

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces.
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan and Daniel Tarlow
arXiv preprint arXiv:1906.06062
(2019-06-14)
dblp.uni-trier.dePDF

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