---
title: "Razvan Pascanu — all works — Elamur Research"
description: "All 27 works of Razvan Pascanu in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/razvan-pascanu/works/"
---
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# Razvan Pascanu — all works

27 works · [← Razvan Pascanu](https://md.research.elamur.ai/free/en/authors/razvan-pascanu/)

1. **[Progressive Neural Networks ↗](https://arxiv.org/abs/1606.04671) arXiv:1606.04671 · no summary yet**Andrei A. Rusu et al.*2016 · 1,295 citations*
2. [**Theano: new features and speed improvements arXiv:1211.5590**Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, et al.*2012 · 1,111 citations*](https://md.research.elamur.ai/free/en/papers/bastien-2012-theano-new-features-speed-improvements/)
3. **[Identifying and attacking the saddle point problem in high-dimensional non-convex optimization ↗](https://arxiv.org/abs/1406.2572) arXiv (Cornell University) · no summary yet**Yann Dauphin et al.*2014 · 818 citations*
4. **[On the Number of Linear Regions of Deep Neural Networks ↗](https://arxiv.org/abs/1402.1869) arXiv (Cornell University) · no summary yet**Guido Montúfar et al.*2014 · 811 citations*
5. [**A simple neural network module for relational reasoning arXiv:1706.01427**Adam Santoro, David Raposo, David G. T. Barrett, et al.*2017 · 632 citations*](https://md.research.elamur.ai/free/en/papers/santoro-2017-simple-neural-network-module-relational/)
6. **[Interaction Networks for Learning about Objects, Relations and Physics ↗](https://arxiv.org/abs/1612.00222) arXiv:1612.00222 · no summary yet**Peter W. Battaglia et al.*2016 · 581 citations*
7. **[Learning Deep Generative Models of Graphs ↗](https://arxiv.org/abs/1803.03324) arXiv (Cornell University) · no summary yet**Yujia Li et al.*2018 · 422 citations*
8. **[Learning to Navigate in Complex Environments ↗](https://arxiv.org/abs/1611.03673) arXiv (Cornell University) · no summary yet**Piotr Mirowski et al.*2016 · 366 citations*
9. [**Progress & Compress: A scalable framework for continual learning arXiv:1805.06370**Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, et al.*2018 · 338 citations*](https://md.research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/)
10. [**How to Construct Deep Recurrent Neural Networks arXiv:1312.6026**Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, et al.*2013 · 302 citations*](https://md.research.elamur.ai/free/en/papers/pascanu-2013-construct-deep-recurrent-neural-networks/)
11. **[Imagination-Augmented Agents for Deep Reinforcement Learning ↗](https://arxiv.org/abs/1707.06203) arXiv (Cornell University) · no summary yet**Théophane Weber et al.*2017 · 241 citations*
12. **[Meta-Learning with Latent Embedding Optimization ↗](https://arxiv.org/abs/1807.05960) arXiv (Cornell University) · no summary yet**Andrei A. Rusu et al.*2018 · 213 citations*
13. **[Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks ↗](https://doi.org/10.1007/978-3-662-44848-9_34) Lecture notes in computer science · no summary yet**Çağlar Gülçehre et al.*2014 · 171 citations*
14. **[Relational Deep Reinforcement Learning ↗](https://arxiv.org/abs/1806.01830) arXiv (Cornell University) · no summary yet**Vinícius Zambaldi et al.*2018 · 160 citations*
15. **[Deep reinforcement learning with relational inductive biases ↗](https://openalex.org/W2907502844) International Conference on Learning Representations · no summary yet**Vinícius Zambaldi et al.*2018 · 132 citations*
16. **[Natural Neural Networks ↗](https://arxiv.org/abs/1507.00210) arXiv (Cornell University) · no summary yet**Desjardins, Guillaume et al.*2015 · 131 citations*
17. [**Resurrecting Recurrent Neural Networks for Long Sequences arXiv:2303.06349**Antonio Orvieto, Samuel L Smith, Albert Gu, et al.*2023 · 118 citations*](https://md.research.elamur.ai/free/en/papers/orvieto-2023-resurrecting-recurrent-neural-networks-long/)
18. **[Policy Distillation ↗](https://arxiv.org/abs/1511.06295) arXiv (Cornell University) · no summary yet**Andrei A. Rusu et al.*2015 · 108 citations*
19. **[Learning model-based planning from scratch ↗](https://arxiv.org/abs/1707.06170) arXiv (Cornell University) · no summary yet**Razvan Pascanu et al.*2017 · 76 citations*
20. **[Metacontrol for Adaptive Imagination-Based Optimization ↗](https://arxiv.org/abs/1705.02670) arXiv (Cornell University) · no summary yet**Jessica B. Hamrick et al.*2017 · 46 citations*
21. **[Relational recurrent neural networks ↗](https://arxiv.org/abs/1806.01822) arXiv (Cornell University) · no summary yet**Adam Santoro et al.*2018 · 43 citations*
22. **[Memory-based Parameter Adaptation ↗](https://arxiv.org/abs/1802.10542) arXiv (Cornell University) · no summary yet**Pablo Sprechmann et al.*2018 · 42 citations*
23. **[Pointer Graph Networks ↗](https://arxiv.org/abs/2006.06380) arXiv:2006.06380 · no summary yet**Petar Veličković et al.*2020 · 25 citations*
24. **[RecurrentGemma: Moving Past Transformers for Efficient Open Language Models ↗](https://arxiv.org/abs/2404.07839) arXiv (Cornell University) · no summary yet**Aleksandar Botev et al.*2024 · 22 citations*
25. **[Learned-norm pooling for deep neural networks. ↗](https://arxiv.org/abs/1311.1780) arXiv (Cornell University) · no summary yet**Çağlar Gülçehre et al.*2013 · 6 citations*
26. **[Transferring Task Goals via Hierarchical Reinforcement Learning ↗](https://openalex.org/W2942204155) no summary yet**Saining 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 ↗](https://openalex.org/W7100233341) no summary yet**Razvan Pascanu et al.*2016*