Continual learning [1], also known as lifelong learning, refers to the ability of an artificial intelligence system to continuously learn and adapt from new experiences over time. This is an important capability as it allows AI models to acquire new knowledge and skills as more data becomes available, without forgetting previously learned information. Continual learning … full description “Algorithms for lifelong deep learning (available)”
Supervisor: Anand Subramoney
Active: updated 5-MAR-2024 (OSAA035) no expiry
Exploring optimization algorithms for recurrent neural networks (available)
Recurrent neural networks (RNNs) are a key type of architecture in modern deep learning, particularly for processing sequential data such as text, speech, video, and time series data. Unlike feedforward networks, RNNs have loops that allow information to persist and be passed from one step to the next. This enables them to effectively model patterns … full description “Exploring optimization algorithms for recurrent neural networks (available)”
Implementation of biologically inspired efficient deep learning models (available)
As deep learning models continue to grow in size and complexity to tackle increasingly difficult tasks, the need for efficient and scalable models becomes ever more important. Extremely large language models like GPT-4 require massive computational resources and expensive hardware to train and run. This makes them impractical to deploy at scale in many real-world … full description “Implementation of biologically inspired efficient deep learning models (available)”