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..
Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing
Authors: Weizhi Gao, Zhichao Hou, Han Xu, Xiaorui Liu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and ablation studies on image recognition demonstrate that our algorithm can significantly accelerate the certification of DEQs by up to 7x almost without sacrificing the certified accuracy. |
| Researcher Affiliation | Academia | Weizhi Gao1 EMAIL Zhichao Hou1 EMAIL Han Xu2 EMAIL Xiaorui Liu1* EMAIL 1North Carolina State University, 2The University of Arizona *corresponding author |
| Pseudocode | Yes | Algorithm 1 Certified Radius with SRS-DEQ |
| Open Source Code | Yes | Our code is available at https://github.com/Weizhi Gao/Serialized-Randomized-Smoothing. |
| Open Datasets | Yes | Datasets. We use two classical datasets in image recognition, CIFAR-10 (Krizhevsky et al., 2009) and Image Net (Russakovsky et al., 2015), to evaluate the certified robustness. |
| Dataset Splits | No | The paper mentions training data and test data. It does not explicitly specify a validation split size or methodology. It discusses results based on different noise levels and number of layers, but not a validation set specifically. |
| Hardware Specification | Yes | All the experiments are conducted on one A100 GPU. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For the standard MDEQ on CIFAR10, we use the Anderson solver with the step of {1, 5, 30}. For the standard MDEQ on Image Net, we use the Broyden solver with the step of {1, 5, 14}. We apply Anderson and Naive solvers on CIFAR-10 and Broyden solver on Image Net for the proposed SRS-MDEQ with the step of {1, 3}. We adopt a warm-up technique, where we use multi-steps to solve the fixed-point problem for the first batch in Algorithm 1. The warm-up steps for our SRS-MDEQ are set as 30 and 14 steps for CIFAR-10 and Image Net, respectively. The failure rate as α = 0.001 and the sampling number as N = 10,000 in the Monte Carlo method, unless specified otherwise. |