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..
Mimetic Initialization of Self-Attention Layers
Authors: Asher Trockman, J Zico Kolter
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We explore the weights of such pre-trained Transformers (particularly for vision) to attempt to find reasons for this discrepancy. Surprisingly, we find that simply initializing the weights of self-attention layers so that they look more like their pre-trained counterparts allows us to train vanilla Transformers faster and to higher final accuracies, particularly on vision tasks such as CIFAR-10 and Image Net classification, where we see gains in accuracy of over 5% and 4%, respectively. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2Bosch Center for AI. |
| Pseudocode | No | The paper provides mathematical formulas for its initialization scheme but does not present them in a pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or links to a code repository. |
| Open Datasets | Yes | Our initialization shows strong advantages for Vi Ts, allowing gains of up to 5% when training on small datasets like CIFAR-10, and up to 4% for larger datasets, i.e., Image Net1k within a standard Res Net-style training pipeline. We also see smaller performance gains on language modeling tasks such as Wiki Text-103. To further show that our initialization is not overfit to CIFAR-10 or Image Net in particular, we present results for CIFAR-100, SVHN, and Tiny Image Net using our initialization. |
| Dataset Splits | No | The paper mentions training on various datasets and conducting ablations, but it does not specify the exact training/validation/test splits (e.g., percentages or counts) used for reproducibility. It implies standard splits but doesn't state them. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper describes the training pipeline and parameters, but it does not specify software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | Yes | Setup We train all Vi Ts using a simple pipeline: we use Rand Augment and Cutout for augmentation, a batch size of 512, Adam W with 3 10 3 learning rate, 0.01 weight decay, and 100 epochs. We use a vanilla Vi T with embedding dimension 192, depth 12, patch size 2, and input size 32 unless otherwise noted (Vi T-Tiny). We use a class token and sinusoidal position embeddings. We use α1 = β1 = 0.7 and α2 = β2 = 0.4 for all experiments. |