Despite the intriguing performances of the deep neural networks, they are far away from human intelligence, evidenced by their vulnerability to transformation. Moreover, the neural networks should be trained on a large dataset for multiple iterations to learn the complex probability distribution, while humans can infer from few examples. Also, training a neural network towards a new objective alters the learned representation, a phenomenon called catastrophic forgetting. This project is aimed at exploring the frontiers of few-shot learning, including the use of graph neural networks and amortized inference strategies. While previous works focused on few-shot learning accuracy, I would also like to emphasize whether the model can learn new information without disrupting previous knowledge.

I decided to participate in SuperUROP after two years of great research experience in the machine learning field. I hope I will gain more insights into the field after this extensive research project.