Projects

1. Effect of Sampling on Robustness of LIME

• LIME(Local Interpretable Model- Agnostic Explanations) suffers from lack of robustness

• Presented a modification of LIME based on CTGAN sampling and analysed it on a popular adversarial attack. 

• Demonstrated better robustness of the algorithm by comparing its explanations to vanilla LIME’s and calculating the percentage of time it could recognize adversaries

2. Clustering Guided GPUCB

• Presented a geometric view of GP-UCB based on posterior mean and variance 

• Introduced two clustering-based acquisition functions GPUCB_NN & GPUCB^2

• Reduced the search space of the GP-UCB acquisition function to a single best cluster chosen by these methods

  Reference

3. Phishing Detection using Classical ML & ensemble of classical Quantam ML models

1. Lithography Hotspot Detection

• Explored ICCAD 2012 benchmark dataset and used it to detect lithography hotspots using Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs) and Vision Transformer (ViT) 

• ViTs gave an overall accuracy of 98.05% which is 1.39% higher than accuracy given by CNNs and 2.04% better than accuracy of ANNs

• While comparing to other works we found that ViTs prove the best in terms of overall accuracy, but at dataset level its performance can be improved for two out of five datasets

2. Empirical Laws of Natural Language Processing for Neural Language Generated Text

• Pre-processed and explored data, tuned hyperparameters to generate text using Long Short Term Memory Networks (LSTMs) and Generative Pretrained Transformer-2 (GPT-2)

• Results showed that the text by LSTMs and GPT-2 followed Zipf’s law and Heap’s law 

• The LSTM generated text improved as the value of hyper-parameter Temperature increased

• The comparison between GPT-2 and LSTM generated text showed that text generated using GPT-2 is more grammatically accurate than that generated by LSTMs

3. Analysis and Classification of Bike Share Data