perturbations

[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection

Implementation of Papers on Adversarial Examples

Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

Single-Cell (Perturbation) Model Library

Differentiable Optimizers with Perturbations in Pytorch

[CVPR 2018] Tensorflow implementation of NAG : Network for Adversary Generation

[ICLR'24] Official PyTorch Implementation of ContraLSP

[ICML'24] Official PyTorch Implementation of TimeX++

NPI Ephemeris Propagation Tool with Uncertainty Extrapolation

CONCERT predicts niche-aware perturbation responses in spatial transcriptomics

Universal Adversarial Audio Perturbations

Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"

Space Engineering 3 Course Work at University of Sydney

Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation

Building a multi-label classifier from scratch and using transfer learning for the PASCAL VOC image dataset.

Adversarial Attack using a DCGAN

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.

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/)

DeepDefend is an open-source Python library for adversarial attacks and defenses in deep learning models, enhancing the security and robustness of AI systems.