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..
Area Attention
Authors: Yang Li, Lukasz Kaiser, Samy Bengio, Si Si
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate area attention on two tasks: neural machine translation (both character and token-level) and image captioning, and improve upon strong (state-of-the-art) baselines in all the cases. |
| Researcher Affiliation | Industry | 1Google Research, Mountain View, CA, USA. Correspondence to: Yang Li <EMAIL>. |
| Pseudocode | Yes | We present the Pseudo code for performing Eq. 3, 4 and 5 as well as the shape size of each area in Algorithm 1 and 2. |
| Open Source Code | Yes | See Tensor Flow implementation of Area Attention as well as its integration with Transformer and LSTM in https://github.com/tensorflow/tensor2tensor. |
| Open Datasets | Yes | We use the same dataset as the one used in (Vaswani et al., 2017) in which the WMT 2014 English-German (EN-DE) dataset contains about 4.5 million English-German sentence pairs, and the English-French (EN-FR) dataset has about 36 million English-French sentence pairs (Wu et al., 2016). |
| Dataset Splits | Yes | we trained each model based on the training & development sets provided by the COCO dataset (Lin et al., 2014), which as 82K images for training and 40K for validation. |
| Hardware Specification | Yes | trained on one machine with 8 NVIDIA P100 GPUs for a total of 250,000 steps. |
| Software Dependencies | No | The paper mentions "Tensor Flow implementation" but does not specify a version number for TensorFlow or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | Tiny (#hidden layers=2, hidden size=128, filter size=512, #attention heads=4), Small (#hidden layers=2, hidden size=256, filter size=1024, #attention heads=4), Base (#hidden layers=6, hidden size=512, filter size=2048, #attention heads=8) and Big (#hidden layers=6, hidden size=1024, filter size=4096 for EN-DE and 8192 for EN-FR, #attention heads=16). |