Luca Pasa

Artificial Intelligence · 2014–2015

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Model Architectures — 12 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Optimization & Training — 9 citations into it
• Signal Processing — 7 citations into it

Whom Luca Pasa trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. James Martens
5. Sepp Hochreiter
6. Paul J. Werbos
7. Nicolas Boulanger-Lewandowski
8. L. V. D. van der Maaten
9. J. Urgen Schmidhuber
10. Alex Krizhevsky

Papers from Luca Pasa's perspective

Ordered by how strongly the researchers Luca Pasa 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

• Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models (2015) — 15 citations
• Pre-training of Recurrent Neural Networks via Linear Autoencoders (2014) — 41 citations