Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Learning an efficient update rule from data that promotes rapid learning of new tasks from
the same distribution remains an open problem in meta-learning. Typically, previous works …

In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned
from previous tasks. However, a critical challenge in meta-learning is task uncertainty and …

The recent progress in neural architecture search (NAS) has allowed scaling the automated
design of neural architectures to real-world domains, such as object detection and semantic …

We propose a meta-learning approach that learns from multiple tasks in a transductive
setting, by leveraging the unlabeled query set in addition to the support set to generate a …

This paper presents a method to interpret the success of knowledge distillation by
quantifying and analyzing task-relevant and task-irrelevant visual concepts that are encoded …

Meta-learning empowers artificial intelligence to increase its efficiency by learning how to
learn. Unlocking this potential involves overcoming a challenging meta-optimisation …