Predicting ATP binding sites for protein sequences
Creating a model for protein-ATP binding sites prediction based on sequence information.
Creating a model for protein-ATP binding sites prediction based on sequence information.
Compared supervised, semi-supervised and self-supervised approaches on image classification task on STL 10 dataset. Used a ResNet-18 model for supervised, vanilla CNN for semi-supervised and SimCLR for unsupervised learning.
This project aims to implement various Meta Learning methods and benchmark them on few-shot learning tasks. The goal is to build a modular extensible and well documented library of these methods in JAX.
Trained a classifier model on SNLI Dataset to determine the inference relationship between the sentence pairs and performed probing on thee model’s layers for POS tags.
Developing a model to perform code search, syntax error detection and code repair on different programming languages.