Implementation of Papers on Adversarial Examples
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Updated
Apr 24, 2023 - Python
Implementation of Papers on Adversarial Examples
[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection
A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.
NPI Ephemeris Propagation Tool with Uncertainty Extrapolation
[CVPR 2018] Tensorflow implementation of NAG : Network for Adversary Generation
Universal Adversarial Audio Perturbations
Space Engineering 3 Course Work at University of Sydney
Differentiable Optimizers with Perturbations in Pytorch
Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks
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.
Tropical Sensitivity Scores
Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"
[ICLR'24] Official PyTorch Implementation of ContraLSP
[ICML'24] Official PyTorch Implementation of TimeX++
The MATLAB model here presented performs trajectory propagations based on the Constant Density Polyhedron algorithm. With this model, it is possible to compute trajectories in the proximity of astronomical bodies such as asteroids or comets. The algorithm also allows to compute ballistic trajectories.
A deep convolutional neural network is used to explain the results of another one (VGG19).
Building a multi-label classifier from scratch and using transfer learning for the PASCAL VOC image dataset.
Single-Cell (Perturbation) Model Library
Perturbation experiments on the latent capsules of 3D Point Capsule Networks by Zhao et al.
Dark photon conversions in our inhomogeneous Universe. Code repository associated with the papers https://arxiv.org/abs/2002.05165 and https://arxiv.org/abs/2004.06733.
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