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..
Feature learning in deep classifiers through Intermediate Neural Collapse
Authors: Akshay Rangamani, Marius Lindegaard, Tomer Galanti, Tomaso A Poggio
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we conduct an empirical study of the feature learning process in deep classifiers. |
| Researcher Affiliation | Academia | 1Center for Brains, Minds, and Machines, Massachusetts Institute of Technology. Correspondence to: Akshay Rangamani <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for running our experiments is available at https://github.com/ mariuslindegaard/Intermediate_Neural_ Collapse/tree/ICML2023 |
| Open Datasets | Yes | We consider the MNIST (Le Cun et al., 1998), Fashion MNIST (Xiao et al., 2017), CIFAR10 (Krizhevsky & Hinton, 2009), and SVHN (Netzer et al., 2011) datasets. |
| Dataset Splits | No | The paper mentions 'batch-size of 128' and '350 training epochs' but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments were conducted on a cluster with NVIDIA Tesla V100, Ge Force GTX 1080 TI, and A100 GPUs. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) to ensure reproducibility. |
| Experiment Setup | Yes | This includes a logarithmic learning rate sweep between 0.0001 and 0.25 decayed twice by a factor of 0.1,a momentum of 0.9, a weight decay of 5e-4, for 350 training epochs, and batch-size of 128. |