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

3 September 2023

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

7 / 7 papers shown

Title
Multilevel Generative Samplers for Investigating Critical Phenomena Ankur Singha E. Cellini K. Nicoli K. Jansen Stefan Kühn Shinichi Nakajima 54 1 0 11 Mar 2025
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics Gert Aarts Kenji Fukushima Tetsuo Hatsuda Andreas Ipp S. Shi L. Wang K. Zhou AI4CE PINN 41 2 0 09 Jan 2025
On learning higher-order cumulants in diffusion models Gert Aarts Diaa E. Habibi L. Wang K. Zhou 26 4 0 28 Oct 2024
Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects Andrea Bulgarelli E. Cellini K. Jansen Stefan Kühn A. Nada Shinichi Nakajima K. Nicoli M. Panero 18 4 0 18 Oct 2024
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 60 1 0 27 May 2024
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,424 0 23 Jan 2020
Pixel Recurrent Neural Networks Aaron van den Oord Nal Kalchbrenner Koray Kavukcuoglu SSeg GAN 225 2,543 0 25 Jan 2016