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..
Evolving Attention with Residual Convolutions
Authors: Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have demonstrated consistent improvement in various natural language and computer vision tasks. |
| Researcher Affiliation | Collaboration | 1Peking University 2Microsoft Research 3Institute of Information Engineering, Chinese Academy of Sciences 4ETH Zurich 5Tsinghua University. |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | The code is available at https://github.com/pkuyym/Evolving Attention |
| Open Datasets | Yes | We choose GLUE benchmark (Wang et al., 2018) for an empirical study. |
| Dataset Splits | Yes | We leverage 10% training data to choose the hyper-parameters and perform evaluation on the development set. |
| Hardware Specification | Yes | All models are trained by 1.28 million training images for 100 epochs on 8 TESLA V100 GPUs. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify version numbers for any software libraries, frameworks (e.g., TensorFlow, PyTorch), or programming languages used. |
| Experiment Setup | Yes | Major hyper-parameters are as follows: optimizer is SGD with momentum 0.9, batch size is 32 per worker, weight decay is 1e-4. For the ο¬rst 5 epochs, the learning rate is scaled linearly from 0 to 0.128, and then it is divided by 10 at epoch 30, 60, 80 and 90. |