Project
Natural Language Inference
Solved NLI problem using logistic regression and DL based approach to compare
SRGAN
Implemented SRGAN for upsampling images. Tried different architecture and training processes to improve performance
Speaker Recognition
Worked on GMM-UBM and advanced I-vector based approaches for speaker recognition
CNN Pruning
Implemented filter prunning for CNN and proposed similarity based approach to further prune model without affecting performance
Improving sample complexity of HRL algorithms
Worked with team to make learning of higher level policy faster by making transitions more denser by considering sub-transitions.
Word Recognition
Considered phoneme level Hidden Markov Model (HMM) with continuous gaussian distribution for word recognition