Tiago Ramalho

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 78 researchers and is trusted by 91 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #3,171 of 35,345 by trust here, #439 by citations
• Computer Vision and Pattern Recognition — 39 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it; #1,521 of 1,525 by trust here, #688 by citations
• Reinforcement Learning — 16 citations into it
• Generative Models — 15 citations into it
• Model Architectures — 13 citations into it; #1,990 of 2,278 by trust here, #328 by citations
• Optimization & Training — 13 citations into it; #785 of 2,601 by trust here, #61 by citations

Whom Tiago Ramalho trusts overall

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

1. J. Urgen Schmidhuber
2. Yoshua Bengio
3. Yann LeCun
4. Geoffrey E. Hinton
5. Sepp Hochreiter
6. Alex Graves
7. Diederik P. Kingma
8. Oriol Vinyals
9. Gregory R. Koch
10. Alex Krizhevsky

Who trusts Tiago Ramalho overall

1. Zirui Wang
2. Yaqing Wang
3. Fei Mi
4. Sanket Vaibhav Mehta
5. Quanming Yao
6. Rahaf Aljundi
7. Vincenzo Lomonaco
8. James T. Kwok
9. Fei Ye
10. Siyuan Shan

Papers from Tiago Ramalho's perspective

Ordered by how strongly the researchers Tiago Ramalho 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Overcoming catastrophic forgetting in neural networks (2016) — 10,384 citations
• Hybrid computing using a neural network with dynamic external memory (2016) — 1,765 citations
• Conditional Neural Processes (2018) — 864 citations
• Adaptive Posterior Learning: few-shot learning with a surprise-based memory module (2019) — 83 citations
• Reply to Huszár: The elastic weight consolidation penalty is empirically valid (2018) — 16 citations