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..
Vision Transformers provably learn spatial structure
Authors: Samy Jelassi, Michael Sander, Yuanzhi Li
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we empirically verify that a Vi T with positional attention performs similarly to the original one on CIFAR-10/100, SVHN and Image Net. |
| Researcher Affiliation | Academia | Samy Jelassi Princeton University EMAIL Michael E. Sander ENS, CNRS EMAIL Yuanzhi Li Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See supplementary material. |
| Open Datasets | Yes | On the experimental side, we validate in Section 6 that Vi Ts learn spatial structure in images from the CIFAR-100 dataset... competitive with the vanilla Vi T on the Image Net, CIFAR-10/100 and SVHNs datasets. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix. |
| Software Dependencies | No | The paper mentions 'Adam W' as an optimizer but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | For the small datasets, we use a Vi T with 7 layers, 12 heads and hidden/MLP dimension 384. For Image Net, we train a 'Vi T-tiny-patch16-224' [24]. Both models are trained with standard augmentations techniques [18] and using Adam W with a cosine learning rate scheduler. We run all the experiments for 300 epochs, with batch size 1024 for Imagenet and 128 otherwise and average our results over 5 seeds. We refer to Appendix A for the training details. |