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..
Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective
Authors: Andi Zhang, Mingtian Zhang, Damon Wischik
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments, Table 1: Success rate (%) of the methods on MNIST., Table 2: Success rate (%) of the methods on Imagenet. |
| Researcher Affiliation | Academia | Andi Zhang Computer Laboratory University of Cambridge EMAIL, Mingtian Zhang Centre for Artificial Intelligence University College London EMAIL, Damon Wischik Computer Laboratory University of Cambridge EMAIL |
| Pseudocode | Yes | Algorithm 1 Sampling from padv by EBM, Algorithm 2 Sampling from padv by diffusion model, Algorithm 3 Rejection Sampling and Sample Refinement, Algorithm 4 Sampling from padv by diffusion model |
| Open Source Code | Yes | Code can be found at https://github.com/andiac/ Adv PP. |
| Open Datasets | Yes | We use MNIST [25] and Image Net [9] in this work. The MNIST dataset is available under the terms of the Creative Commons Attribution-Share Alike 3.0 license. |
| Dataset Splits | No | The paper mentions using images from the MNIST test set and refers to adversarially trained models, but does not explicitly detail the train/validation splits used for its own experiments. |
| Hardware Specification | Yes | We conducted our experiments using multiple workstations, each equipped with an NVIDIA RTX 4090 GPU (24GB VRAM) and 64GB of system memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or CUDA versions). |
| Experiment Setup | Yes | Our method is evaluated across three hyperparameter configurations: c = 5, c = 10, and c = 20. |