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 [1].
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. |