Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling. - Kim Nicoli; Pan Kessel; Nils Strodthoff; Wojciech Samek; Klaus-Robert Müller; Shinichi Nakajima (CoRR, 2019)

Asymptotically Unbiased Generative Neural Sampling. - Kim A. Nicoli; Shinichi Nakajima; Nils Strodthoff; Wojciech Samek; Klaus-Robert Müller; Pan Kessel (CoRR, 2019)

On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models. - Kim A. Nicoli; Christopher J. Anders; Lena Funcke; Tobias Hartung; Karl Jansen; Pan Kessel; Shinichi Nakajima; Paolo Stornati (CoRR, 2020)

Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse. - Kim A. Nicoli; Christopher J. Anders; Lena Funcke; Tobias Hartung; Karl Jansen; Pan Kessel; Shinichi Nakajima; Paolo Stornati (CoRR, 2021)

Path-Gradient Estimators for Continuous Normalizing Flows. - Lorenz Vaitl; Kim Andrea Nicoli; Shinichi Nakajima; Pan Kessel (ICML, 2022)

Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows. - Lorenz Vaitl; Kim A. Nicoli; Shinichi Nakajima; Pan Kessel (Mach. Learn. Sci. Technol., 2022)

Physics-Informed Bayesian Optimization of Variational Quantum Circuits. - Kim Nicoli; Christopher J. Anders; Lena Funcke; Tobias Hartung; Karl Jansen; Stefan Kühn; Klaus-Robert Müller; Paolo Stornati; Pan Kessel; Shinichi Nakajima (NeurIPS, 2023)

Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories. - Kim A. Nicoli; Christopher J. Anders; Tobias Hartung; Karl Jansen; Pan Kessel; Shinichi Nakajima (CoRR, 2023)

Deep generative models for thermodynamics of spin systems and field theories - Kim Andrea Nicoli (2023)

Adaptive Observation Cost Control for Variational Quantum Eigensolvers. - Christopher J. Anders; Kim Andrea Nicoli; Bingting Wu; Naima Elosegui; Samuele Pedrielli; Lena Funcke; Karl Jansen; Stefan Kühn; Shinichi Nakajima (ICML, 2024)

Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects. - Andrea Bulgarelli; Elia Cellini; Karl Jansen; Stefan Kühn; Alessandro Nada; Shinichi Nakajima; Kim A. Nicoli; Marco Panero (CoRR, 2024)

Multilevel Generative Samplers for Investigating Critical Phenomena. - Ankur Singha; Elia Cellini; Kim Andrea Nicoli; Karl Jansen; Stefan Kühn; Shinichi Nakajima (ICLR, 2025)

Simulating the Hubbard Model with Equivariant Normalizing Flows. - Dominic Schuh; Janik Kreit; Evan Berkowitz; Lena Funcke; Thomas Luu; Kim A. Nicoli; Marcel Rodekamp (CoRR, 2025)

Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers. - Kim A. Nicoli; Luca J. Wagner; Lena Funcke (CoRR, 2025)

Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers. - Samuele Pedrielli; Christopher J. Anders; Lena Funcke; Karl Jansen; Kim A. Nicoli; Shinichi Nakajima (CoRR, 2025)

SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows. - Janik Kreit; Dominic Schuh; Kim A. Nicoli; Lena Funcke (CoRR, 2025)

Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows. - Dominic Schuh; Janik Kreit; Evan Berkowitz; Lena Funcke; Thomas Luu; Kim A. Nicoli; Marcel Rodekamp (CoRR, 2025)