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Razvan Pascanu
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Overcoming catastrophic forgetting in neural networks
J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, ...
Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017
80932017
On the difficulty of training recurrent neural networks
R Pascanu
arXiv preprint arXiv:1211.5063, 2013
77782013
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
39072018
Progressive neural networks
AA Rusu, NC Rabinowitz, G Desjardins, H Soyer, J Kirkpatrick, ...
arXiv preprint arXiv:1606.04671, 2016
31192016
On the number of linear regions of deep neural networks
GF Montufar, R Pascanu, K Cho, Y Bengio
Advances in neural information processing systems 27, 2014
28292014
Theano: a CPU and GPU math expression compiler
J Bergstra, O Breuleux, F Bastien, P Lamblin, R Pascanu, G Desjardins, ...
Proceedings of the Python for scientific computing conference (SciPy) 4 (3), 1-7, 2010
20302010
A simple neural network module for relational reasoning
A Santoro, D Raposo, DG Barrett, M Malinowski, R Pascanu, P Battaglia, ...
Advances in neural information processing systems 30, 2017
19322017
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
YN Dauphin, R Pascanu, C Gulcehre, K Cho, S Ganguli, Y Bengio
Advances in neural information processing systems 27, 2014
18592014
Theano: new features and speed improvements
F Bastien, P Lamblin, R Pascanu, J Bergstra, I Goodfellow, A Bergeron, ...
arXiv preprint arXiv:1211.5590, 2012
16982012
Interaction networks for learning about objects, relations and physics
P Battaglia, R Pascanu, M Lai, D Jimenez Rezende
Advances in neural information processing systems 29, 2016
16902016
Meta-learning with latent embedding optimization
AA Rusu, D Rao, J Sygnowski, O Vinyals, R Pascanu, S Osindero, ...
arXiv preprint arXiv:1807.05960, 2018
16642018
How to construct deep recurrent neural networks
R Pascanu
arXiv preprint arXiv:1312.6026, 2013
14152013
Learning to navigate in complex environments
P Mirowski, R Pascanu, F Viola, H Soyer, AJ Ballard, A Banino, M Denil, ...
arXiv preprint arXiv:1611.03673, 2016
10182016
Progress & compress: A scalable framework for continual learning
J Schwarz, W Czarnecki, J Luketina, A Grabska-Barwinska, YW Teh, ...
International conference on machine learning, 4528-4537, 2018
9572018
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv e-prints, arXiv: 1605.02688, 2016
9302016
Model compression via distillation and quantization
A Polino, R Pascanu, D Alistarh
arXiv preprint arXiv:1802.05668, 2018
8732018
Theano: A CPU and GPU Math Compiler in Python.
J Bergstra, O Breuleux, F Bastien, P Lamblin, R Pascanu, G Desjardins, ...
SciPy, 18-24, 2010
8682010
Policy distillation
AA Rusu, SG Colmenarejo, C Gulcehre, G Desjardins, J Kirkpatrick, ...
arXiv preprint arXiv:1511.06295, 2015
8302015
Sharp minima can generalize for deep nets
L Dinh, R Pascanu, S Bengio, Y Bengio
International Conference on Machine Learning, 1019-1028, 2017
8142017
Understanding the exploding gradient problem
R Pascanu
arXiv preprint arXiv:1211.5063, 2012
7902012
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Artikelen 1–20