Danny Tarlow

Mila > À propos de Mila > Équipe > Danny Tarlow
Membre Industriel Principal
Danny Tarlow
Professeur adjoint, McGill University, Google
Danny Tarlow

Danny Tarlow est chercheur scientifique chez Google Brain. Il s’intéresse principalement aux méthodes d’apprentissage automatique pour comprendre et générer des programmes. Il est également professeur auxiliaire à l’École d’informatique de l’Université McGill. Titulaire d’un doctorat du groupe d’apprentissage automatique de l’Université de Toronto, il a passé quatre ans comme post-doctorant puis chercheur chez Microsoft Research, à Cambridge, avant de venir à Montréal.

Publications

2021-07

Structured Denoising Diffusion Models in Discrete State-Spaces.
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow and Rianne van den Berg
arXiv preprint arXiv:2107.03006
(2021-07-07)
dblp.uni-trier.dePDF

2021-06

Learning to Combine Per-Example Solutions for Neural Program Synthesis.
Disha Shrivastava, Hugo Larochelle and Daniel Tarlow
arXiv preprint arXiv:2106.07175
(2021-06-14)
dblp.uni-trier.dePDF
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
Daniel D. Johnson, Jacob Austin, Rianne van den Berg and Daniel Tarlow

2021-05

Learning to Extend Program Graphs to Work-in-Progress Code.
Xuechen Li, Chris J. Maddison and Daniel Tarlow
arXiv preprint arXiv:2105.14038
(2021-05-28)
ui.adsabs.harvard.eduPDF

2020-12

Tabular: Probabilistic Inference from the Spreadsheet
Andrew D. Gordon, Claudio Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel and Daniel Tarlow
(venue unknown)
(2020-12-01)
www.cambridge.org

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

2020-07

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)
ui.adsabs.harvard.eduPDF

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
Gradient Estimation with Stochastic Softmax Tricks
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause and Chris J. Maddison

2020-04

LEARNING EXECUTION THROUGH NEURAL CODE FUSION
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan and Milad Hashemi

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)
ui.adsabs.harvard.eduPDF

2020-01

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

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)
ui.adsabs.harvard.eduPDF

Publications collected and formatted using Paperoni

array(1) { ["wp-wpml_current_language"]=> string(2) "fr" }