Adversarial AI
Authors: Yevgeniy Vorobeychik
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation showed that this approach is significantly more robust to evasion than alternatives (including several previous adversarial learning methods), with only a small sacrifice in accuracy if no evasion attacks occur. |
| Researcher Affiliation | Academia | Yevgeniy Vorobeychik Electrical Engineering and Computer Science Vanderbilt University yevgeniy.vorobeychik@vanderbilt.edu |
| Pseudocode | No | The paper describes algorithmic concepts but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., repository links, explicit statements of code release, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper mentions 'training data' in a general context (e.g., 'sufficient training data is collected'), but it does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset used in its discussions of past work. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper mentions the use of 'computationally expensive protein modeling tools' but does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments. |
| Software Dependencies | No | The paper mentions a software tool ('Rosetta') but does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate experiments. |
| Experiment Setup | No | The paper describes general approaches and techniques but does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |