Doing analysis on shared embedding space for the natural languages of English and Tamil
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Updated
Apr 18, 2018 - Jupyter Notebook
Doing analysis on shared embedding space for the natural languages of English and Tamil
A stochastic input pre-processing technique based on a process of down-sampling/up-sampling using convolution and transposed convolution layers. Defending convolutional neural network against adversarial attacks.
Program that uses tensorflow and keras to recognize images and by the way has an extra code to confuse said artificial intelligence with fake recreated images. Technologies and languages used: Jupyter, Tensorflow, Keras and Python. Own learning.
Talk presented during 3rd SeComp from UTFPR, Brazil, Apucarana. This repository contains all codes, slides, and supplementary material.
Notes, tutorials, code snippets and templates focused on Generative Adversarial NNs for Machine Learning
Adversarial attacks on CNNs using gradients of the network
Binary Iterative Method for Non Adversarial Attack
[Master Thesis] Research project at the Data Analytics Lab in collaboration with Daedalean AI. The thesis was submitted to both ETH Zürich and Imperial College London.
Adversarial sentence generation and robustness training for Natural Language tasks
AGV-Project for evolutionary adversarial attacks on XAI methods
A new kind of MLOps platform purpose built for production generative ai apps
Here we visualize the need for robust BO against an adversary. Clearly the optimum design point changes depending the uncertain parameter x, so we should identify a region for which the decision variable x resides in an optimal region.
Projects for CS-839 Topics in Security (Spring 2018)
Implementation of Adversarial attack to generate adversarial samples of text that are misclassified by the LSTM based Classifier.
A PyTorch Implementation of DCGAN on a dataset of celebrity faces.
Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.
FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids
Benchmarking and Visualization Tool for Adversarial Machine Learning
Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, to solve inference tasks that were not seen during training, these models (EGMs) often need to be…
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