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..
Learning Neural Network Subspaces
Authors: Mitchell Wortsman, Maxwell C Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present experimental results across benchmark datasets for image classification (CIFAR-10 (Krizhevsky et al., 2009), Tiny-Image Net (Le & Yang, 2015), and Image Net (Deng et al., 2009)) for various residual networks (He et al., 2016; Zagoruyko & Komodakis, 2016). |
| Researcher Affiliation | Collaboration | 1University of Washington (work completed during internship at Apple). 2Apple. |
| Pseudocode | Yes | Algorithm 1 Train Subspace |
| Open Source Code | Yes | Code available at https: //github.com/apple/learning-subspaces. |
| Open Datasets | Yes | The CIFAR-10 (Krizhevsky et al., 2009) and Tiny-Image Net (Le & Yang, 2015), and Image Net (Deng et al., 2009) experiments follow Frankle et al. (2020) |
| Dataset Splits | No | The paper describes training parameters and mentions standard datasets but does not explicitly state the use of a validation set or its specific split percentage/methodology for its experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions software like PyTorch and MXNet in its references, but it does not specify the version numbers for any software components used in its experiments. |
| Experiment Setup | Yes | The CIFAR-10 (Krizhevsky et al., 2009) and Tiny-Image Net (Le & Yang, 2015) experiments follow Frankle et al. (2020) in training for 160 epochs using SGD with learning rate 0.1, momentum 0.9, weight decay 1e-4, and batch size 128. For Image Net we follow Xie et al. (2019) in changing batch size to 256 and weight decay to 5e-5. All experiments are conducted with a cosine annealing learning rate scheduler (Loshchilov & Hutter, 2016) with 5 epochs of warmup and without further regularization (unless explicitly mentioned). |