Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Multilevel Generative Samplers for Investigating Critical Phenomena
Authors: Ankur Singha, Elia Cellini, Kim A. Nicoli, Karl Jansen, Stefan Kühn, Shinichi Nakajima
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the effective sample size of Ri GCS is a few orders of magnitude higher than state-of-the-art generative model baselines in sampling configurations for 128 128 two-dimensional Ising systems. |
| Researcher Affiliation | Academia | 1BIFOLD, Germany, 2 Technische Universit at Berlin, Germany 3Universit a degli Studi di Torino, Italy, 4 INFN Torino, Italy, 5University of Bonn, Germany 6Helmholtz Institute for Radiation and Nuclear Physics (HISKP) 7Deutsches Elektronen-Synchrotron (DESY), Germany, 8RIKEN Center for AIP, Japan |
| Pseudocode | Yes | The pseudocodes provided in Algorithm 1 and Algorithm 2 describe the practical steps for training Ri GCS and for sampling from a trained Ri GCS, respectively. |
| Open Source Code | Yes | The code is available at https://github.com/mlneuralsampler/multilevel. |
| Open Datasets | No | The paper uses the two-dimensional Ising model, which is a theoretical model and not a publicly available dataset in the conventional sense. Configurations for this model are generated through simulation, rather than being loaded from a pre-existing data source. |
| Dataset Splits | No | The paper evaluates a simulated physical system (the Ising model) and does not use pre-split datasets for training, validation, or testing in the traditional machine learning context. |
| Hardware Specification | Yes | For all models (Ri GCS and the baselines), we used a single NVIDIA A100 GPU with 80 GB of memory. |
| Software Dependencies | No | The paper mentions using the ADAM optimizer and Pixel CNN architecture, but does not provide specific version numbers for software libraries or frameworks (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We trained VANs for 50000 gradient updates (steps) with batch size 100, and HANs for 100000 gradient updates with batch size 1000. For Ri GCS, training is performed for a total of 3000 steps for each sequential (upscaled) target lattice. When training on a target lattice NL = N, the pretraining phase involves training at coarser levels as follows: 2000 steps for level L 2, 1500 steps for level L 4, and 1000 steps for all previous levels, except for the coarsest one which is always trained for 500 steps. |