🧑‍🔬 Razvan Pascanu — all works

27 works · ← Razvan Pascanu

1. Progressive Neural Networks ↗arXiv:1606.04671 · no summary yetAndrei A. Rusu et al.2016 · 1,295 citations
2. Theano: new features and speed improvementsarXiv:1211.5590Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, et al.2012 · 1,111 citations
3. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization ↗arXiv (Cornell University) · no summary yetYann Dauphin et al.2014 · 818 citations
4. On the Number of Linear Regions of Deep Neural Networks ↗arXiv (Cornell University) · no summary yetGuido Montúfar et al.2014 · 811 citations
5. A simple neural network module for relational reasoningarXiv:1706.01427Adam Santoro, David Raposo, David G. T. Barrett, et al.2017 · 632 citations
6. Interaction Networks for Learning about Objects, Relations and Physics ↗arXiv:1612.00222 · no summary yetPeter W. Battaglia et al.2016 · 581 citations
7. Learning Deep Generative Models of Graphs ↗arXiv (Cornell University) · no summary yetYujia Li et al.2018 · 422 citations
8. Learning to Navigate in Complex Environments ↗arXiv (Cornell University) · no summary yetPiotr Mirowski et al.2016 · 366 citations
9. Progress & Compress: A scalable framework for continual learningarXiv:1805.06370Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, et al.2018 · 338 citations
10. How to Construct Deep Recurrent Neural NetworksarXiv:1312.6026Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, et al.2013 · 302 citations
11. Imagination-Augmented Agents for Deep Reinforcement Learning ↗arXiv (Cornell University) · no summary yetThéophane Weber et al.2017 · 241 citations
12. Meta-Learning with Latent Embedding Optimization ↗arXiv (Cornell University) · no summary yetAndrei A. Rusu et al.2018 · 213 citations
13. Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks ↗Lecture notes in computer science · no summary yetÇağlar Gülçehre et al.2014 · 171 citations
14. Relational Deep Reinforcement Learning ↗arXiv (Cornell University) · no summary yetVinícius Zambaldi et al.2018 · 160 citations
15. Deep reinforcement learning with relational inductive biases ↗International Conference on Learning Representations · no summary yetVinícius Zambaldi et al.2018 · 132 citations
16. Natural Neural Networks ↗arXiv (Cornell University) · no summary yetDesjardins, Guillaume et al.2015 · 131 citations
17. Resurrecting Recurrent Neural Networks for Long SequencesarXiv:2303.06349Antonio Orvieto, Samuel L Smith, Albert Gu, et al.2023 · 118 citations
18. Policy Distillation ↗arXiv (Cornell University) · no summary yetAndrei A. Rusu et al.2015 · 108 citations
19. Learning model-based planning from scratch ↗arXiv (Cornell University) · no summary yetRazvan Pascanu et al.2017 · 76 citations
20. Metacontrol for Adaptive Imagination-Based Optimization ↗arXiv (Cornell University) · no summary yetJessica B. Hamrick et al.2017 · 46 citations
21. Relational recurrent neural networks ↗arXiv (Cornell University) · no summary yetAdam Santoro et al.2018 · 43 citations
22. Memory-based Parameter Adaptation ↗arXiv (Cornell University) · no summary yetPablo Sprechmann et al.2018 · 42 citations
23. Pointer Graph Networks ↗arXiv:2006.06380 · no summary yetPetar Veličković et al.2020 · 25 citations
24. RecurrentGemma: Moving Past Transformers for Efficient Open Language Models ↗arXiv (Cornell University) · no summary yetAleksandar Botev et al.2024 · 22 citations
25. Learned-norm pooling for deep neural networks. ↗arXiv (Cornell University) · no summary yetÇağlar Gülçehre et al.2013 · 6 citations
26. Transferring Task Goals via Hierarchical Reinforcement Learning ↗no summary yetSaining Xie et al.2018 · 3 citations
27. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013. Polyak, Boris Teodorovich. Some methods of speeding up the convergence of iteration methods ↗no summary yetRazvan Pascanu et al.2016

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