Giacomo Bassetto

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #10,190 of 35,345 by trust here, #19,018 by citations
• Probabilistic & Bayesian Methods — 22 citations into it; #667 of 1,525 by trust here, #904 by citations
• Statistics and Probability — 19 citations into it; #3,594 of 12,932 by trust here, #4,809 by citations
• Cognitive Neuroscience — 10 citations into it; #5,189 of 10,852 by trust here, #7,600 by citations
• Statistical and Nonlinear Physics — 6 citations into it

Whom Giacomo Bassetto trusts overall

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

1. Radford M. Neal
2. Max Welling
3. Simon N. Wood
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Edward Meeds
7. Alex Graves
8. George Papamakarios
9. Michael U Gutmann
10. Yoshua Bengio

Who trusts Giacomo Bassetto overall

1. Joeri Hermans
2. Justin Alsing
3. Conor Durkan
4. J. Brehmer
5. George Papamakarios
6. Gilles Louppe
7. David S. Greenberg
8. Volodimir Begy
9. Benjamin Dan Wandelt
10. Iain Murray

Papers from Giacomo Bassetto's perspective

Ordered by how strongly the researchers Giacomo Bassetto 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Flexible statistical inference for mechanistic models of neural dynamics (2017) — 334 citations
• Likelihood-free inference with emulator networks (2018) — 155 citations