Equivariant Transformer is all you need

Efficient langevin simulation of coupled classical fields and fermions

Kipton Barros and Yasuyuki Kato · 2013

Earlier work this paper cites.

Self-learning monte carlo method

Junwei Liu, Yang Qi, Zi Yang Meng, and Liang Fu · 2017

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Self-learning monte carlo method and cumulative update in fermion systems

Junwei Liu, Huitao Shen, Yang Qi, Zi Yang Meng, and Liang Fu · 2017

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Self-learning monte carlo with deep neural networks

Huitao Shen, Junwei Liu, and Liang Fu · 2018

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Scaling laws for neural language models, 2020

Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020

Earlier work this paper cites.

Gauge covariant neural network for 4 dimensional non-abelian gauge theory

Yuki Nagai and Akio Tomiya · 2021

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Highly accurate protein structure prediction with alphafold

J. Jumper, R. Evans, A. Pritzel, et al · 2021

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An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021

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Physics-informed machine learning

G. E. Karniadakis, I. G. Kevrekidis, L. Lu, et al · 2021

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Effective Ruderman–Kittel–Kasuya–Yosida-like interaction in diluted double-exchange model: Self-learning monte carlo approach

Hidehiko Kohshiro and Yuki Nagai · 2021

Cited alongside, same era.

TorchMD-NET: Equivariant transformers for neural network based molecular potentials

Philipp Thölke and Gianni De Fabritiis · 2022

Cited alongside, same era.

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky · 2022

Later among the works it cites.

Sample generation for the spin-fermion model using neural networks

Georgios Stratis, Phillip Weinberg, Tales Imbiriba, Pau Closas, and Adrian E Feiguin · 2022

Later among the works it cites.

Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, and Phiala E. Shanahan · 2023

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E(n)-Equivariant Graph Neural Networks Emulating Mesh-Discretized Physics

Masanobu Horie · 2023

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Self-learning monte carlo with equivariant transformer, 2023

Yuki Nagai and Akio Tomiya · 2023

Closest in time.