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..
Understanding Backdoor Attacks through the Adaptability Hypothesis
Authors: Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark image datasets and state-of-the-art backdoor attacks for deep neural networks are conducted to corroborate the hypothesis. Theoretical analyses of backdoor attacks under classical machine learning context. |
| Researcher Affiliation | Collaboration | 1Department of ECE, University of Minnesota 2School of Statistics, University of Minnesota 3Cisco Research 4Carlson School of Management, University of Minnesota. |
| Pseudocode | Yes | G. Pseudo-code for visualisation algorithms and additional experimental results. Algorithm 1 Visualizing high-dimensional data |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing its code or a link to a code repository. |
| Open Datasets | Yes | We use 3 popular datasets: MNIST (Le Cun et al., 2010), CIFAR10 (Krizhevsky et al., 2009), and GTSRB (Stallkamp et al., 2012). |
| Dataset Splits | No | The paper mentions training and test sets but does not provide specific details about a validation split, explicit percentages for data partitioning, or cross-validation setup. |
| Hardware Specification | Yes | All of our experiments are conducted on a workstation with one A100 GPU. |
| Software Dependencies | No | The paper mentions machine learning models (Le Net, Res Net, VGG) and optimization algorithms (SGD) but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For Res Net and VGG models, we adopt the standard training pipeline of SGD with a momentum of 0.9, a weight decay of 10-4, and a batch size of 128 for optimization. For Let Net, we adopt the standard training pipeline of SGD with the initial learning rate of 0.1/0.01. |