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..
When Are Solutions Connected in Deep Networks?
Authors: Quynh N. Nguyen, Pierre Bréchet, Marco Mondelli
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Numerical Experiments We compare the losses along the path of Theorem 4.1 and the one in [24] which our theoretical analysis has improved upon. |
| Researcher Affiliation | Academia | Quynh Nguyen MPI-MIS, Germany EMAIL Pierre Bréchet MPI-MIS, Germany EMAIL Marco Mondelli IST Austria EMAIL |
| Pseudocode | No | The paper describes procedural steps, particularly in the Proof of Theorem 4.1, but does not present any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code for reproducing the results is available at https://github.com/Quynh-Nguyen/mode_connectivity |
| Open Datasets | Yes | Datasets and architectures. We consider MNIST [26] and CIFAR-10 [23] datasets. |
| Dataset Splits | No | The paper mentions using standard SGD for training but does not explicitly provide details about training/validation/test dataset splits, such as percentages, sample counts, or specific split methodologies. |
| Hardware Specification | Yes | All experiments were run on a single machine with a NVIDIA GeForce RTX 2080 Ti GPU and an Intel i7-8700K CPU. |
| Software Dependencies | Yes | Our code is written in Python using PyTorch v1.7.0. |
| Experiment Setup | Yes | We train each network by standard SGD with cross-entropy loss, batch size 100 and no explicit regularizers. ... The learning rate is set to 0.01 for the MNIST experiments and to 0.1 for the CIFAR-10 experiments. We use a step decay schedule for the learning rate, dividing it by 10 at epochs 50 and 75 (total 100 epochs). |