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..
Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space
Authors: Linyi Li, Zexuan Zhong, Bo Li, Tao Xie
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation results show that our approach can provide significantly better provable adversarial error bounds on MNIST and CIFAR10 datasets, compared to the state-of-the-art results. |
| Researcher Affiliation | Academia | Linyi Li , Zexuan Zhong , Bo Li and Tao Xie University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | No | The paper describes algorithms and formulations but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code and model weights are available at https://github.com/llylly/Robustra. |
| Open Datasets | Yes | We evaluate Robustra on image classification tasks with two datasets: MNIST [Le Cun et al., 1998] and CIFAR10 [Krizhevsky and Hinton, 2009]. |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' but does not explicitly describe a validation split or its size/percentage. |
| Hardware Specification | Yes | All experiments are run on Geforce GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'SGD optimizer' but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | For each model and dataset, we run 100 epochs on the training set. Adam optimizer is used for MNIST models, and SGD optimizer (0.9 momentum, 5 10 4 weight decay) is used for CIFAR10 models. The ℓ norm ϵ is initialized by 0.01, and then it linearly increases to the configured ϵ (0.1 or 0.3 for MNIST, 2/255 or 8/255 for CIFAR10) in the first 20 epochs. In the first 20 epochs, the learning rate is set to be 0.001; then, it decades by half every 10 epochs. The batch size is set to 50. |