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..
Recurrent Models of Visual Attention
Authors: Volodymyr Mnih, Nicolas Heess, Alex Graves, koray kavukcuoglu
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on several image classification tasks, where it significantly outperforms a convolutional neural network baseline on cluttered images, and on a dynamic visual control problem, where it learns to track a simple object without an explicit training signal for doing so. |
| Researcher Affiliation | Industry | Volodymyr Mnih Nicolas Heess Alex Graves Koray Kavukcuoglu Google Deep Mind {vmnih,heess,gravesa,korayk} @ google.com |
| Pseudocode | No | The paper describes the model architecture and training procedure in text and diagrams but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a link or statement about open-sourcing the code for the described methodology. |
| Open Datasets | Yes | We first tested the ability of our training method to learn successful glimpse policies by using it to train RAM models with up to 7 glimpses on the MNIST digits dataset. |
| Dataset Splits | No | The paper mentions using a 'test set' but does not specify explicit training, validation, or test split percentages or counts for the datasets used. |
| Hardware Specification | No | The paper mentions training on 'multiple GPUs' but does not provide specific hardware details such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper describes software components conceptually (e.g., 'stochastic gradient descent') but does not specify software names with version numbers. |
| Experiment Setup | Yes | All methods were trained using stochastic gradient descent with minibatches of size 20 and momentum of 0.9. We annealed the learning rate linearly from its initial value to 0 over the course of training. Hyperparameters such as the initial learning rate and the variance of the location policy were selected using random search [3]. |