Alvaro Sanchez-Gonzalez

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 12 papers; trusts 168 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 289 citations into it; #13,654 of 35,345 by trust here, #4,772 by citations
• Computer Vision and Pattern Recognition — 157 citations into it; #13,010 of 21,575 by trust here, #5,711 by citations
• Graph Learning — 81 citations into it; #361 of 693 by trust here, #56 by citations
• Reinforcement Learning — 77 citations into it
• Model Architectures — 58 citations into it
• Representation & Self-Supervised Learning — 49 citations into it
• Statistical and Nonlinear Physics — 48 citations into it
• Computational Theory and Mathematics — 31 citations into it

Whom Alvaro Sanchez-Gonzalez trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Jimmy Ba
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Peter W. Battaglia
9. Richard S. Sutton
10. J. Urgen Schmidhuber

Who trusts Alvaro Sanchez-Gonzalez overall

1. Peter W. Battaglia
2. Miles Cranmer
3. Johannes Brandstetter
4. Sungyong Seo
5. Jessica B. Hamrick
6. Tobias Pfaff
7. Yunzhu Li
8. Victor Bapst
9. Zijie Li
10. Zong-Yi Li

Papers from Alvaro Sanchez-Gonzalez's perspective

Ordered by how strongly the researchers Alvaro Sanchez-Gonzalez 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Graph networks as learnable physics engines for inference and control (2018) — 677 citations
• Learning to Simulate Complex Physics with Graph Networks (2020) — 1,550 citations
• Structured agents for physical construction (2019) — 107 citations
• Learning Mesh-Based Simulation with Graph Networks (2020) — 1,318 citations
• Hamiltonian Graph Networks with ODE Integrators (2019) — 195 citations
• WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models (2023) — 360 citations
• GraphCast: Learning skillful medium-range global weather forecasting (2022) — 419 citations
• ETA Prediction with Graph Neural Networks in Google Maps (2021) — 295 citations
• Discovering Symbolic Models from Deep Learning with Inductive Biases (2020) — 676 citations
• Relational inductive biases, deep learning, and graph networks (2018) — 3,656 citations
• CompILE: Compositional Imitation Learning and Execution (2018) — 139 citations
• Solar flux distribution on central receivers: A projection method from analytic function (2015) — 149 citations