Adrià Garriga-Alonso

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 54 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #13,799 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #9,008 of 21,575 by trust here, #10,905 by citations
• Probabilistic & Bayesian Methods — 15 citations into it; #832 of 1,525 by trust here, #781 by citations
• Model Architectures — 14 citations into it
• Optimization & Training — 8 citations into it
• Statistical and Nonlinear Physics — 7 citations into it

Whom Adrià Garriga-Alonso trusts overall

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

1. Kaiming He
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Radford M. Neal
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Xavier Glorot
8. X. Zhang
9. Yann LeCun
10. Ilya Sutskever

Who trusts Adrià Garriga-Alonso overall

1. Greg Yang
2. Timothy Nguyen
3. Guillermo Valle Pérez
4. Simon Kornblith
5. Roman Novak
6. Jaehoon Lee
7. Nikola B. Kovachki
8. Lechao Xiao
9. Zhourung Chen
10. Chico Q. Camargo

Papers from Adrià Garriga-Alonso's perspective

Ordered by how strongly the researchers Adrià Garriga-Alonso 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Deep Convolutional Networks as shallow Gaussian Processes (2018) — 286 citations
• Understanding Variational Inference in Function-Space (2020) — 51 citations
• Towards Automated Circuit Discovery for Mechanistic Interpretability (2023) — 720 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations