Projects

Build a precise and scalable automated verifier for proving the robustness of fully connected and convolutional neural networks against adversarial attacks

Implemented Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks on networks trained over CIFAR10 and MNIST.

Formulated suitable parameters on all the three parameters of Geometric Skew Normal distribution to perform Bayesian analysis and evaluated the fit on multiple datasets using Kolmogorov-Smirnov test statistic.

Analysed Dirichlet-Multinomial Regression (DMR) model for topic modeling with metadata. Derived and implemented Stochastic Gradient Riemann Langevin Dynamics on DMR model.

Build an automatic review generator and restaurant recommender system