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..
The Lipschitz Constant of Self-Attention
Authors: Hyunjik Kim, George Papamakarios, Andriy Mnih
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the practical relevance of our theoretical work, we formulate invertible self-attention and use it in a Transformer-based architecture for a characterlevel language modelling task. We compare its test log-likelihood and stability to dot-product self-attention. |
| Researcher Affiliation | Industry | 1Deep Mind, UK. Correspondence to: Hyunjik Kim <EMAIL>. |
| Pseudocode | No | The paper describes algorithms and mathematical formulations in text and equations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | at character-level language modelling on the Penn Treebank dataset (Marcus et al., 1993). |
| Dataset Splits | Yes | tuning the hyperparameters on a validation set. |
| Hardware Specification | No | This was the deepest model we could fit on a single GPU, and we expect to be able to train even deeper models with these two. (Section 5.4) - This mentions a GPU but no specific model or specifications are provided. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | In practice, this leads to instabilities in training for DP-MHA, hence requiring careful tuning of the learning rate schedule for training deeper Transformer models: linear warmup and square root decay, as detailed in Appendix H. |