Meta-Learning: Learning to Learn with Neural Networks Internship

About Internship

Professor, Department of CSE, from Chandigarh University, Mohali, is accepting interns interested in the field of Computer Science Engineering, in the research area of Artificial intelligence and Machine learning.

This proposed work aims to design and evaluate a meta-learning framework that enables neural networks to rapidly adapt to new tasks with minimal data and computational resources. The research focuses on improving neural networks' ability to generalize across diverse tasks using Model-Agnostic Meta-Learning (MAML) and related optimization techniques. Objectives: Task Distribution: Develop a range of tasks that simulate real-world scenarios, such as image recognition, natural language processing, and reinforcement learning. These tasks will train the meta-learning model to extract generalizable learning patterns. Model Implementation: Build a meta-learning model using MAML, which allows the network to learn task-agnostic parameters. The goal is for the model to adapt quickly to new tasks by fine-tuning with minimal data. Few-Shot Learning: Test the model’s ability to perform few-shot learning by evaluating how effectively it adapts to unseen tasks with limited data. Its performance will be compared against traditional models that rely on extensive training data. Optimization Enhancements: Experiment with optimization techniques, including second-order gradient methods and alternative loss functions, to improve the model's learning speed and adaptability. Application to Real-World Problems: Apply the meta-learning framework to personalized healthcare, where quick adaptation to new patients with limited medical data is essential. This will assess the model's ability to operate in dynamic, real-world environments. This research aims to advance meta-learning by enhancing neural networks' flexibility and efficiency, enabling faster and more reliable learning in data-constrained situations.