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].
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. |