Giuseppe Carleo
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 6 papers; trusts 25 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 154 citations into it; #25,514 of 35,345 by trust here, #10,207 by citations
- Atomic and Molecular Physics, and Optics — 148 citations into it; #782 of 7,135 by trust here, #1,029 by citations
- Materials Chemistry — 49 citations into it
- Statistical and Nonlinear Physics — 42 citations into it
- Condensed Matter Physics — 34 citations into it
- Computer Vision and Pattern Recognition — 29 citations into it
- Probabilistic & Bayesian Methods — 26 citations into it
- Model Architectures — 16 citations into it
Whom Giuseppe Carleo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Radford M. Neal
- E. Gardner
- S. White
- Gilles Louppe
- Yoshua Bengio
- Patrick T. Komiske
- Juan Felipe Carrasquilla
- Ulrike von Luxburg
Who trusts Giuseppe Carleo overall
- David Pfau
- Juan Felipe Carrasquilla
- Wei Ma
- Göran Wendin
- Marcello Benedetti
- K. Ch'ng
- Peter Broecker
- Ivan Glasser
- Stig Petersen
- Marios Mattheakis
Papers from Giuseppe Carleo's perspective
Ordered by how strongly the researchers Giuseppe Carleo trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Machine learning and the physical sciences (2019) — 1,895 citations
- Deep autoregressive models for the efficient variational simulation of many-body quantum systems (2019) — 252 citations
- Quantum Natural Gradient (2019) — 537 citations
- Quantum Simulators: Architectures and Opportunities (2019) — 496 citations
- Neural-network quantum state tomography (2017) — 901 citations
- Solving the quantum many-body problem with artificial neural networks (2016) — 2,329 citations