DeepDefend is an open-source Python library for adversarial attacks and defenses in deep learning models, enhancing the security and robustness of AI systems.
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Updated
Oct 10, 2024 - Python
DeepDefend is an open-source Python library for adversarial attacks and defenses in deep learning models, enhancing the security and robustness of AI systems.
A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.
Single-Cell (Perturbation) Model Library
Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks
NPI Ephemeris Propagation Tool with Uncertainty Extrapolation
Scalable Expressiveness through Preprocessed Graph Perturbations (CIKM 2024)
Some resummation techniques based on perturbation theory
[ICML'24] Official PyTorch Implementation of TimeX++
[ICLR'24] Official PyTorch Implementation of ContraLSP
A Character-level Perturbation Generator based on probability distribution, density and diversity.
This is the enhanced version of Stadius Move on Thionville, the traffic info scraping bot for Citéline buses.
Personal notes explore various aspects of cosmology, with a particular focus on the dynamics of a homogeneous and isotropic universe.
Implementation of Papers on Adversarial Examples
Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"
In this work, we extend the FGSM method proposing multistep adversarial perturbation (MSAP) procedures to study the recommenders’ robustness under powerful methods. Letting fixed the perturbation magnitude, we illustrate that MSAP is much more harmful than FGSM in corrupting the recommendation performance of BPR-MF.
Fast Gradient Sign Method Adversarial Attack on Digit Recognition Model
Code and raw data for the implementation of "Correction of human forehead temperature variations measured by non-contact infrared thermometer". Adrian Shajkofci, 2020
Simulate the orbital motion for a CubeSat (HokieNav) as part of a senior design project with VT's ECE department sponsored by The Aerospace Corporation.
Code to analyze high-density EEG and concurrent EMG/EOG datastreams during balance perturbations (replicates results from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088363/)
[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection
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