Cover Image

Convergence of variational Monte Carlo simulation and scale-invariant pre-training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Abrahamsen, Nilin, Ding, Zhiyan, Goldshlager, Gil, Lin, Lin
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
by: Goldshlager, Gil, et al.
Published: (2024)

ISOPO: Proximal policy gradients without pi-old
by: Abrahamsen, Nilin
Published: (2025)

PROMA: Projected Microbatch Accumulation for Reference-Free Proximal Policy Updates
by: Abrahamsen, Nilin
Published: (2026)

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
by: Goldshlager, Gil, et al.
Published: (2025)

Constrained Ensemble Langevin Monte Carlo
by: Ding, Zhiyan, et al.
Published: (2021)

Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing
by: Goldshlager, Gil, et al.
Published: (2025)

Langevin Monte Carlo: random coordinate descent and variance reduction
by: Ding, Zhiyan, et al.
Published: (2020)

Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
by: Schätzle, Zeno, et al.
Published: (2025)

Operator-Level Quantum Acceleration of Non-Logconcave Sampling
by: Leng, Jiaqi, et al.
Published: (2025)

Refined Gradient-Based Temperature Optimization for the Replica-Exchange Monte-Carlo Method
by: Miyata, Tatsuya, et al.
Published: (2026)

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
by: Liang, Jiajun, et al.
Published: (2024)

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
by: Szabó, P. Bernát, et al.
Published: (2026)

Quantum speedup of non-linear Monte Carlo problems
by: Blanchet, Jose, et al.
Published: (2025)

Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization
by: Guzmán-Cordero, Andrés, et al.
Published: (2025)

True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration
by: Qinghua, et al.
Published: (2026)

A simulation-based training framework for machine-learning applications in ARPES
by: Na, MengXing, et al.
Published: (2025)

Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study
by: Chai, Shengdu, et al.
Published: (2025)

Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling
by: Christiansen, Henrik, et al.
Published: (2023)

Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem
by: Qiao, Bei, et al.
Published: (2026)

Learning Permutation-invariant Macroscopic Dynamics
by: Han, Zhichao, et al.
Published: (2026)

Fluctuation without dissipation: Microcanonical Langevin Monte Carlo
by: Robnik, Jakob, et al.
Published: (2023)

Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative
by: Wang, Zhe, et al.
Published: (2024)

Counterdiabatic Hamiltonian Monte Carlo
by: Cohn-Gordon, Reuben, et al.
Published: (2026)

Elements of Sequential Monte Carlo
by: Naesseth, Christian A., et al.
Published: (2019)

Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations
by: Perzanowski, Andrzej, et al.
Published: (2024)

High pressure hydrogen by machine learning and quantum Monte Carlo
by: Tirelli, Andrea, et al.
Published: (2021)

Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning
by: Lu, Minglei, et al.
Published: (2024)

Coreset Markov Chain Monte Carlo
by: Chen, Naitong, et al.
Published: (2023)

LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning
by: Ban, Kayoung, et al.
Published: (2024)

SCAN: Self-Denoising Monte Carlo Annotation for Robust Process Reward Learning
by: Ding, Yuyang, et al.
Published: (2025)

Some Best Practices in Operator Learning
by: Enyeart, Dustin, et al.
Published: (2024)

Loss Terms and Operator Forms of Koopman Autoencoders
by: Enyeart, Dustin, et al.
Published: (2024)

Tail-Sensitive KL and Rényi Convergence of Unadjusted Hamiltonian Monte Carlo via One-Shot Couplings
by: Bou-Rabee, Nawaf, et al.
Published: (2026)

Kinetic Interacting Particle Langevin Monte Carlo
by: Oliva, Paul Felix Valsecchi, et al.
Published: (2024)

Combining Normalizing Flows and Quasi-Monte Carlo
by: Andral, Charly
Published: (2024)

Data-efficient pre-training by scaling synthetic megadocs
by: Kim, Konwoo, et al.
Published: (2026)

DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM Performance
by: Cohen, Seffi, et al.
Published: (2025)

Curriculum Learning for Mesh-based simulations
by: Garnier, Paul, et al.
Published: (2025)

From pre-training to downstream performance: Does domain-specific pre-training make sense?
by: Krones, Felix
Published: (2026)

Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
by: Sahin, M. Emre, et al.
Published: (2025)