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..
Robust low-rank training via approximate orthonormal constraints
Authors: Dayana Savostianova, Emanuele Zangrando, Gianluca Ceruti, Francesco Tudisco
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This is shown by extensive numerical evidence and by our main approximation theorem that shows the computed robust low-rank network well-approximates the ideal full model, provided a highly performing low-rank sub-network exists. ... We provide several experimental evaluations on different architectures and datasets, where the robust low-rank networks are compared against a variety of baselines. |
| Researcher Affiliation | Academia | Dayana Savostianova Gran Sasso Science Institute 67100 L Aquila (Italy) EMAIL Emanuele Zangrando Gran Sasso Science Institute 67100 L Aquila (Italy) EMAIL Gianluca Ceruti University of Innsbruck 6020 Innsbruck (Austria) EMAIL Francesco Tudisco Gran Sasso Science Institute 67100 L Aquila (Italy) EMAIL |
| Pseudocode | Yes | Algorithm 1: Pseudocode of robust well-Conditioned Low-Rank (Cond LR ) training scheme |
| Open Source Code | Yes | All the experiments can be reproduced with the code in Py Torch available at https://github.com/COMPi LELab/Cond LR. |
| Open Datasets | Yes | We consider MNIST , CIFAR10, and CIFAR100 [33] datasets for evaluation purposes. ... [33] A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. 2009. |
| Dataset Splits | No | The paper mentions "60,000 training images" and "10,000 test images" for MNIST, and similar counts for CIFAR10/100, but does not specify any explicit validation dataset splits (e.g., percentages or exact counts for a validation set). |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" as the framework used for the code, but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Each method and model was trained for 120 epochs of stochastic gradient descent with a minibatch size of 128. We used a learning rate of 0.1 for Le Net5 and 0.05 for VGG16 with momentum 0.3 and 0.45, respectively, and a learning rate scheduler with factor = 0.3 at 70 and 100 epochs. |