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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural Persistence Dynamics
Authors: Sebastian Zeng, Florian Graf, Martin Uray, Stefan Huber, Roland Kwitt
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model Neural Persistence Dynamics substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks. |
| Researcher Affiliation | Collaboration | University of Salzburg, Austria Josef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our publicly available reference implementation can be found at https://github.com/plus-rkwitt/neural_persistence_dynamics. |
| Open Datasets | Yes | Our publicly available reference implementation can be found at https://github.com/plus-rkwitt/neural_persistence_dynamics. ... For reproducibility, we will release the simulation data publicly. |
| Dataset Splits | Yes | We randomly partition each dataset into ο¬ve training/testing splits of size 80/20. |
| Hardware Specification | Yes | All experiments were run on an Ubuntu Linux system (22.04), running kernel 5.15.0-100-generic, with 34 Intel Core i9-10980XE CPU @ 3.00GHz cores, 128 GB of main memory, and two NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions software components like m TAN architecture, Euler method, ADAM, and Ripser++, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Each model is trained for 150 epochs using ADAM [31] (with a weight decay of 0.001), starting at a learning rate of 0.001 (decaying according to a cosine annealing schedule) and MSE as a reconstruction (i.e., to evaluate the ο¬rst term in Eq. (4)) and regression loss. |