Adrià Puigdomènech Badia

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 9 papers; trusts 119 researchers and is trusted by 570 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 232 citations into it; #718 of 35,345 by trust here, #529 by citations
• Reinforcement Learning — 184 citations into it; #174 of 2,242 by trust here, #71 by citations
• Computer Vision and Pattern Recognition — 77 citations into it
• Model Architectures — 43 citations into it; #2,183 of 2,278 by trust here, #337 by citations
• Cognitive Neuroscience — 42 citations into it
• Natural Language Processing — 37 citations into it
• Management Science and Operations Research — 34 citations into it
• Optimization & Training — 25 citations into it

Whom Adrià Puigdomènech Badia trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Alex Graves
4. Diederik P. Kingma
5. Ronald J. Williams
6. Yoshua Bengio
7. J. Urgen Schmidhuber
8. Alex Krizhevsky
9. Yann LeCun
10. Kaiming He

Who trusts Adrià Puigdomènech Badia overall

1. Sergey Levine
2. Pieter Abbeel
3. Yoshua Bengio
4. Danijar Hafner
5. Yuxi Li
6. Ruslan Salakhutdinov
7. Mohammad Norouzi
8. Trevor Darrell
9. Quoc V. Le
10. Abhinav Kumar Gupta

Papers from Adrià Puigdomènech Badia's perspective

Ordered by how strongly the researchers Adrià Puigdomènech Badia trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Asynchronous Methods for Deep Reinforcement Learning (2016) — 10,169 citations
• Hybrid computing using a neural network with dynamic external memory (2016) — 1,765 citations
• Imagination-Augmented Agents for Deep Reinforcement Learning (2017) — 599 citations
• Neural Episodic Control (2017) — 405 citations
• Agent57: Outperforming the Atari Human Benchmark (2020) — 594 citations
• Memory-based Parameter Adaptation (2018) — 106 citations
• Never Give Up: Learning Directed Exploration Strategies (2020) — 360 citations
• MEMO: A Deep Network for Flexible Combination of Episodic Memories (2020) — 40 citations
• Generalization of Reinforcement Learners with Working and Episodic Memory (2019) — 74 citations