Luca Falorsi

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #29,559 of 35,345 by trust here, #17,110 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #18,181 of 21,575 by trust here, #11,498 by citations
• Generative Models — 21 citations into it; #1,648 of 1,956 by trust here, #1,271 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Luca Falorsi trusts overall

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

1. Diederik P. Kingma
2. Danilo Jimenez Rezende
3. Max Welling
4. Marco Cote
5. Ruslan Salakhutdinov
6. Shakir Mohamed
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. Alex Krizhevsky
10. Radford M. Neal

Who trusts Luca Falorsi overall

1. Nicola De Cao
2. Tongzhou Wang
3. Jiacheng Xu
4. Sebastian Farquhar
5. Wilker Aziz
6. Tatsunori B. Hashimoto
7. Mingda Chen
8. Zhaocheng Zhu
9. Kristopher T. Jensen

Papers from Luca Falorsi's perspective

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

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

Their papers

• Hyperspherical Variational Auto-Encoders (2018) — 473 citations
• Explorations in Homeomorphic Variational Auto-Encoding (2018) — 131 citations