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..
Revisiting Mode Connectivity in Neural Networks with Bezier Surface
Authors: Jie Ren, Pin-Yu Chen, Ren Wang
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method on CIFAR-10, CIFAR-100, and Tiny-Image Net datasets using VGG16, Res Net18, and Vi T architectures. The codes are available at https://github.com/TIML-Group/MCSurface. |
| Researcher Affiliation | Collaboration | Jie Ren1,2, Pin-Yu Chen3, Ren Wang1 1Illinois Institute of Technology, 2University of Wisconsin-Madison, 3IBM Research |
| Pseudocode | Yes | Algorithm 1: Bezier Surface Mode Connectivity Algorithm (Summary) Algorithm 2: Bezier Surface Mode Connectivity Algorithm |
| Open Source Code | Yes | The codes are available at https://github.com/TIML-Group/MCSurface. |
| Open Datasets | Yes | We evaluate our method on three different datasets including CIFAR-10 (Krizhevsky & Hinton, 2009), CIFAR-100 (Krizhevsky & Hinton, 2009), and Tiny Imagenet (Le & Yang, 2015) |
| Dataset Splits | No | The paper uses standard datasets like CIFAR-10, CIFAR-100, and Tiny Imagenet, and mentions evaluating on 'test data' and 'training data'. However, it does not explicitly specify the training/test/validation split percentages, sample counts, or refer to a specific predefined split with citations for these experiments in the main text. |
| Hardware Specification | Yes | The experiments were performed on a single NVIDIA 4090 GPU sampling 80 points per batch on the surface. |
| Software Dependencies | No | The paper does not explicitly mention any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | Algorithm 2: Bezier Surface Mode Connectivity Algorithm Input: Initial weights θ00, θnm, θ0m, and θn0 (fixed four end control points), number of epochs E1, E2, with epoch E = E1 + E2, learning rate η, number of random samples k, training dataset D0, batch size B |