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Bart van MerriŽnboer
Bart van MerriŽnboer
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Title
Cited by
Cited by
Year
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
K Cho, B van Merrienboer, C Gulcehre, F Bougares, H Schwenk, ...
arXiv preprint arXiv:1406.1078, 2014
273542014
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
K Cho, B van MerriŽnboer, D Bahdanau, Y Bengio
arXiv preprint arXiv:1409.1259, 2014
79062014
Towards AI-complete question answering: a set of prerequisite toy tasks
J Weston, A Bordes, S Chopra, T Mikolov, B van MerriŽnboer
arXiv preprint arXiv:1502.05698, 2015
11922015
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv preprint arXiv:1605.02688, 2016
10782016
Blocks and Fuel: Frameworks for deep learning
B van MerriŽnboer, D Bahdanau, V Dumoulin, D Serdyuk, ...
arXiv preprint arXiv:1506.00619, 2015
2032015
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation
J Pouget-Abadie, D Bahdanau, B van MerriŽnboer, K Cho, Y Bengio
arXiv preprint arXiv:1409.1257, 2014
922014
Automatic differentiation in ML: Where we are and where we should be going
B van MerriŽnboer, O Breuleux, A Bergeron, P Lamblin
Advances in neural information processing systems, 8757-8767, 2018
842018
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
B van MerriŽnboer, D Moldovan, A Wiltschko
Advances in Neural Information Processing Systems, 6256-6265, 2018
332018
Gradmax: Growing neural networks using gradient information
U Evci, B van Merrienboer, T Unterthiner, M Vladymyrov, F Pedregosa
arXiv preprint arXiv:2201.05125, 2022
262022
Halting time is predictable for large models: A universality property and average-case analysis
C Paquette, B van MerriŽnboer, E Paquette, F Pedregosa
Foundations of Computational Mathematics 23 (2), 597-673, 2023
162023
Information matrices and generalization
V Thomas, F Pedregosa, B van MerriŽnboer, PA Mangazol, Y Bengio, ...
arXiv preprint arXiv:1906.07774, 2019
142019
Tangent: Automatic Differentiation Using Source Code Transformation in Python
B van MerriŽnboer, AB Wiltschko, D Moldovan
arXiv preprint arXiv:1711.02712, 2017
12*2017
Fast Training of Sparse Graph Neural Networks on Dense Hardware
M Balog, B van MerriŽnboer, S Moitra, Y Li, D Tarlow
arXiv preprint arXiv:1906.11786, 2019
112019
Multiscale sequence modeling with a learned dictionary
B van MerriŽnboer, A Sanyal, H Larochelle, Y Bengio
arXiv preprint arXiv:1707.00762, 2017
112017
Automatic Differentiation in Myia
O Breuleux, B van MerriŽnboer
72017
In Search for a Generalizable Method for Source Free Domain Adaptation
M Boudiaf, T Denton, B van MerriŽnboer, V Dumoulin, E Triantafillou
arXiv preprint arXiv:2302.06658, 2023
52023
Optimizing sparse graph neural networks for dense hardware
DS Tarlow, M Balog, B Van Merrienboer, Y Li, S Moitra
US Patent 11,562,239, 2023
2023
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