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..
Hyperbolic Attention Networks
Authors: Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our models on synthetic and real-world tasks. Experiments where the underlying graph structure is explicitly known clearly show the bene๏ฌts of using hyperbolic geometry as an inductive bias. |
| Researcher Affiliation | Academia | No explicit institutional affiliations or email domains are provided within the text of the paper to classify the authors' affiliations. |
| Pseudocode | No | The paper describes the methods textually and with equations but does not include any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | We use a publicly available version: https://github.com/tensorflow/tensor2tensor |
| Open Datasets | Yes | We evaluate all the models on the WMT14 En-De dataset (Bojar et al., 2014). We use two of the standard graph transduction benchmark datasets, Citeseer and Cora (Sen et al., 2008). |
| Dataset Splits | No | The paper mentions generating training data and using test sets, but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or clear references to predefined splits within the paper's text). |
| 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 mentions using 'Tensor2tensor' in a footnote, but it does not specify version numbers for this or any other software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | We use models with 3 recursive self-attention layers, each of which has 4 heads with 4 units each for each of q, k, and v. |